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Corpus-based Analyses of the Problem–Solution Pattern
Volume 29
Corpus-based Analyses of the Problem–Solution Pattern. A phraseological approachby Lynne Flowerdew
General Editor
Elena Tognini-BonelliThe Tuscan Word Center/ The University of Siena
SCL focuses on the use of corpora throughout language study, the development of a quantitative approach to linguistics, the design and use of new tools for processing language texts, and the theoretical implications of a data-rich discipline.
Studies in Corpus Linguistics (SCL)
Consulting Editor
Wolfgang Teubert
Advisory Board Michael BarlowUniversity of Auckland
Douglas BiberNorthern Arizona University
Marina BondiUniversity of Modena and Reggio Emilia
Christopher S. ButlerUniversity of Wales, Swansea
Sylviane GrangerUniversity of Louvain
M.A.K. HallidayUniversity of Sydney
Susan HunstonUniversity of Birmingham
Stig JohanssonOslo University
Graeme KennedyVictoria University of Wellington
Geoffrey N. LeechUniversity of Lancaster
Anna MauranenUniversity of Helsinki
Ute RömerUniversity of Hannover
Michaela MahlbergUniversity of Liverpool
Jan SvartvikUniversity of Lund
John M. SwalesUniversity of Michigan
Yang HuizhongJiao Tong University, Shanghai
Corpus-based Analyses of the Problem–Solution PatternA phraseological approach
Lynne FlowerdewHong Kong University of Science & Technology
John Benjamins Publishing Company
Amsterdam / Philadelphia
Library of Congress Cataloging-in-Publication Data
Flowerdew, Lynne.Corpus-based analyses of the problem/solution pattern : a phraseological approach /
Lynne Flowerdew. p. cm. (Studies in Corpus Linguistics, issn 1388-0373 ; v. 29)Includes bibliographical references and index.1. Corpora (Linguistics) 2. Grammar, Comparative and general--Data processing. I.
Title.P128.C68F56 2008
415'.0285--dc22 2007031621isbn 978 90 272 2303 6 (Hb; alk. paper)
© 2008 – John Benjamins B.V.No part of this book may be reproduced in any form, by print, photoprint, microfilm, or any other means, without written permission from the publisher.
John Benjamins Publishing Co. · P.O. Box 36224 · 1020 me Amsterdam · The NetherlandsJohn Benjamins North America · P.O. Box 27519 · Philadelphia pa 19118-0519 · usa
The paper used in this publication meets the minimum requirements of American National Standard for Information Sciences – Permanence of Paper for Printed Library Materials, ansi z39.48-1984.
8 TM
� For�my�father�Albert�Frederick�Scovell,�scientist�and�inventor
Table of contents
Acknowledgments xi
chapter�1Problem-Solution pattern: An overview and corpus analytic perspective 1Clause�relations�as�a�means�of�identifying�the�Problem-Solution�pattern� 1Grammatical�signals�of�clause�relations�for�the�Problem-Solution�pattern� 4Lexical�signals�of�clause�relations�for�the�Problem-Solution�pattern� 5Corpus�analysis�of�a�grammatical�signal�for�the�Problem�element� 8Corpus�analysis�of�a�lexical�signal�for�the�Problem�element� 10Conclusion� 11
chapter�2Issues in corpus linguistics and discourse studies 13Methodologies� 14Contextual�features� 15Interpretation�of�data� 16Corpus�linguistics:�Towards�a�multi-faceted�approach� 19
chapter�3The two corpora: Context and compilation 21Contextual�background�of�the�Professional�and�Student�corpus� 21Issues�in�corpus�compilation� 24Conclusion� 32
chapter�4�Frequency, key word and key-key word analysis of signals for the Problem-Solution pattern 33Classificatory�framework�for�signals:�Appraisal�system� 33Frequency�analysis�of�signals� 35Key�word�analysis�of�signals� 39Key-key�word�analysis�of�signals� 44Differences�between�PROFCORP�and�STUCORP� 49Conclusion� 50
viii� Corpus-based�Analyses�of�Problem-Solution�Pattern�
chapter�5PROFCORP: Phraseological analysis of signals for the Problem element 53Classificatory�framework:�Causal�semantic�relations� 53Classificatory�framework:�Lexico-grammatical�patterns� 55Analysis�of�problem�and�problems� 57Analysis�of�need� 62Analysis�of�impacts�and�impact� 63Conclusion� 73
chapter�6PROFCORP: Phraseological analysis of signals for the Solution element 75Classificatory�framework:�Functional�categories�for�nominal�signals� 76Classificatory�framework:�Grammatical�/�causal�categories�for�adjectival�and�verbal�groups� 77Analysis�of�recommendations� 78Analysis�of�solutions�and�solution 80Analysis�of�recommended 82Analysis�of�proposed 89Analysis�of�implementation 92Conclusion� 94
chapter�7STUCORP: Phraseological analysis of signals for the Problem element 97Analysis�of�problem�and�problems 98Analysis�of�need 110Conclusion� 113
chapter�8STUCORP: Phraseological analysis of signals for the Solution element 115Analysis�of�recommendations 115Analysis�of�solutions�and�solution 117Analysis�of�recommended 120Analysis�of�proposed 123Analysis�of�implementation 126Conclusion� 128
� Table�of�contents� ix
chapter�9General conclusions and implications for pedagogy 129Some�principal�findings�from�PROFCORP� 129Expert�vs.�apprentice�writing� 131Pedagogic�implications�and�applications�of�findings� 133Overall�conclusions� 138
Appendices 141References 165Name index 175Subject index 177
Acknowledgments
I�am�greatly� indebted�to�Michael�Hoey�for�his� invaluable�guidance,� inspiration�and�encouragement�in�carrying�out�the�research�for�this�book.�I�am�also�grateful�to�Tony�McEnery�and�Mike�Scott�for�their�insightful�comments�on�an�earlier�draft.�My�thanks�go�to�the�anonymous�reviewer�and�Elena�Tognini-Bonelli,�the�series�editor,�for�all�their�advice.�I�would�also�like�to�thank�Ulla�Connor�for�her�support�and�encouragement�for�my�work�over�the�years.�Colleagues,�Pansy�Lam,�Edward�Li,�Jacqui�Lam�McArthur�and�John�Milton,�have�provided�friendship,�conversa-tions�and�moral�support�over�the�past�15�years,�for�which�I�am�very�grateful.
Last,�but�not�least,�I�wish�to�thank�my�husband�John�and�my�sons,�Rupert�and�Humphrey,�without�whose�constant�support,�encouragement�and�understanding�this�book�might�never�have�been�written.
chapter�1
Problem-Solution pattern An�overview�and�corpus�analytic�perspective
One�of�the�most�common�patterns�of�text�organization�is�the�Problem-Solution�pattern,�comprising�four�main�elements:�Situation,�Problem,�Solution�and�Evalu-ation.�This�pattern�functions�as�the�main�organizing�principle�of�many�different�kinds�of�written�and�spoken�texts�ranging�from�advertisements�to�workplace�re-ports�and�has�been�extensively�studied�by�Hoey�(1983,�1986,�2001)�and�Jordan�(1984)�among�others.�An�annotated�bibliography�of�the�early�work�on�the�Prob-lem-Solution�pattern�by� linguists� such�as�Beardsley,�Becker,�Labov�and�Winter�can�be�found�in�Hoey�(1983:�189–201).�Much�of�the�discussion�and�analysis�of�this�pattern�has�focused�on�clause�relations�as�a�means�of�identifying�the�pattern,�and�also�on�the�grammatical�and�lexical�signals�for�realizing�the�basic�elements�of�the�pattern.�This�introductory�chapter�illustrates�these�key�concepts�and�concludes�by�making�a�case�for�identification�of�the�signals�for�the�Problem-Solution�pattern�using�corpus�analytic�techniques.
Clause relations as a means of identifying the Problem-Solution pattern
Hoey�and�Winter’s�(1986)�starting�point�for�analysis�of�the�Problem-Solution�pat-tern�is�with�how�discourse�is�created�through�clause�relations,�then�moving�on�to�the�ways�in�which�these�clause�relations�are�signaled.�Moreover,�both�Winter�and�Hoey�stress�that�a�clause�relation�is�a�cognitive�process�whereby�the�reader�inter-prets�the�discourse�in�a�particular�way�set�up�by�inferential�connections�made�by�the�writer.�
Besides� the� interpretative�nature�of�clause�relations,�another�observation� is�that�the�clause�relation�does�not�relate�only�to�clauses�or�adjacent�sentences,�but�can�also�refer�to�the�relation�between�two�paragraphs,�which�can�be�seen�as�a�larg-er�clause�relation�(Hoey�1983).�This�aspect�is�important�in�that�it�recognises�that�the�Problem-Solution�pattern�is�not�confined�to�the�level�of�the�clause,�sentence�or�paragraph�(as�was�initially�thought�by�Becker�1965),�but�can�refer�to�any�unit�of�discourse�above�the�level�of�the�clause.�The�observation�that�different�elements�are�
2� Corpus-based�Analyses�of�Problem-Solution�Pattern�
not�necessarily�co-terminous�with�paragraphs,�sentences�or�clauses�can�be�illus-trated�by�the�following�example�for�an�Internet�service�from�Hoey�(2001:�128):
TRYING�TO�WORK�WITH�THE�INTERNET?IS�THE�INTERNET�TURNING�YOU�INTO�A�MONSTER?�LET�MCIS�HELP�YOU�TO�CONTROL�THE�BEAST.MCIS�is�a�Total�Internet�Solution�Provider�and�can�assist�you�in�the�following�areas:�[A�list�follows]�
The�Situation�element�in�the�first�sentence�and�the�Problem�element�in�the�sec-ond� sentence� are� both� co-terminous� with� their� respective� sentences.� However,�the�third�sentence�offers�a�Solution�(MCIS)�as�well�as�a�positive�evaluation�(help)�and�reiterates�the�problem�(beast),�signaled�by�the�near-synonym�monster�in�the�previous�sentence.�In�the�above�example,�the�evaluative�element�is�embedded�in�the�Solution�and�both�the�Problem�and�Solution�elements�extend�across�clauses�and�sentences.
This�nature�of�textual�patterning�has�been�commented�on�by�other�discourse�analysts,�most�notably�McCarthy�(1991):
These�patterns�are�manifested�in�regularly�occurring�functional�relationships�be-tween�bits�of�text.�These�bits�may�be�phrases,�clauses,�sentences�or�groups�of�sen-tences;�we�shall�refer�to�them�as�textual�segments to�avoid�confusion�with�gram-matical�elements�and�syntactic�relations�within�clauses�and�sentences.�A�segment�may�sometimes�be�a�clause,�sometimes�a�sentence,�sometimes�a�whole�paragraph;�what�is�important�is�that�segments�can�be�isolated�using�a�set�of�labels�covering�a�finite�set�of�functional�relations�that�can�occur�between�any�two�bits�of�text.�� (McCarthy�1991:�28)
‘These� functional� relationships� between� bits� of� text’� referred� to� by� McCarthy�above� are� synonymous� with� the� types� of� clause� relations� summarised� in� Hoey�(2001:�30),�namely�Sequence�relations�(e.g.�time,�cause-consequence,�means-pur-pose,� and� premise-deduction)� and� Matching� relations,� which� include� contrast,�similarity,�exemplification,�preview-detail�and�exception.�These�clause�relations�can�themselves�act�as�signals�of�Problem-Solution�patterns�because�these�signal-ling�relationships�regularly�co-occur.�With�specific�reference�to�the�Problem-So-lution�pattern,�Hoey�notes�that�‘…�the�relation�between�Problem�and�Response�is�also�one�of�Cause–Consequence�and�that�between�Response�and�Result�is�also�one� of� Instrument–Achievement’.� (Hoey� uses� the� term� ‘Response’� rather� than�Solution�when�referring�to�this� individual�part�of�the�pattern,�and�employs�the�term�‘Result’�when�a�successful�outcome�to�the�Solution�is�achieved).�However,�it�should�be�noted�that�evidence�of�the�existence�of�the�cause-consequence�relation�
� Chapter�1.� Problem-Solution�pattern� 3
does�not�necessary�entail�evidence�of�the�existence�of�the�Problem-Solution�pat-tern�(Hoey�1983).�
By�way�of� illustration,� in�an�excerpt� from�the�discussion�section�of�a�final-year�undergraduate�engineering�project�report�in�Figure�1-1,�in�the�Problem�1b�+�Solution�pair,�the�cause�is�signalled�by�However,� in�the�first�sentence�and�the�consequence�by�As a result,�in�the�second�sentence.�In�this�example,�there�is�also�an�Instrument–Achievement�pair,�where�the�main�clause�in�the�second�sentence�(…we added an air pump…)�signals�the�Instrument,�and�the�subordinate�clause�(…allowing external air …)�the�Achievement.�
Results�AnalysisModifications Although�we�could�not�test�the�concentration�of�oxygen�in�the�seawater�due�to�equipment�failure�we�could�observe�that�the�fish�in�the�tank�lacked�oxy-gen�as�most�of�them�came�up�to�the�water�surface�for�respiration.�The�origi-nal�air�injection�system�integrated�with�the�filter�could�not�provide�enough�oxygen�to�the�culture.�We�added�an�external�air�pump�to�improve�the�situ-ation.�However,�we�could�not�inject�air�into�the�tank�directly�as�foam�might�form.�As a result,�we�added�an�air�pump�into�the�foam�removal�unit,�allow-ing�external�air�to�be�injected�into�the�unit.
Situation
Problem 1a + partial Solution
Problem 1b +Solution
In�order�to�remove�carbon�dioxide�from�the�culture,�we�put�some�seaweed�in�the�tank.�This�is�the�most�efficient�way�to�remove�carbon�dioxide�from�the�water.
Problem 2 +Solution +Evaluation
Figure 1-1. Example�of�clause�relations�in�the�Problem-Solution�pattern�(Flowerdew�2003:�491)
In�fact,�the�above�extract�in�Figure�1-1�is�a�modification�of�the�pattern,�in�this�case�‘progressive�multilayering’,�where�each�Solution�only�solves�part�of�the�Problem�(see�Hoey�1983:�81–106�for�variations�of�the�basic�pattern).
The�following�section�examines�clause�relations�in�more�detail�to�determine�how�the�clause�relations�(and�hence�the�Problem-Solution�pattern)�are�signalled�grammatically�and�lexically�to�the�reader.�Although�the�means�of�signalling�clause�relations�for�the�Problem-Solution�pattern�have�been�discussed�in�the�literature�under�the�categories�of�elicitation�techniques�(i.e.�questioning�and�paraphrasing),�grammatical�signals,�lexical�signals�and�lexical�repetitions,�I�shall�confine�my�dis-cussion�to�grammatical�and�lexical�signals�as�these�are�the�foci�of�the�computa-tional�analysis�in�this�book.
4� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Grammatical signals of clause relations for the Problem-Solution pattern
The� earliest� work� in� this� area� was� carried� out� by� Winter� (1971,� 1977)� who� il-lustrates� how� certain� closed-set� grammatical� items� such� as� subordinators� and�sentence�connectors�(comprising�adjuncts)�act�as�signalling�devices�for�the�Prob-lem-Solution�pattern.�A�list�of�the�subordinators�and�sentence�connectors,�which�he� terms�Vocabulary�1�and�Vocabulary�2� items�respectively,� is�given� in�Winter�(1977).�I�will�now�examine�some�examples�from�the�literature�where�the�logical�sequence�of�Instrument–Achievement�clause�relations,�which�as�stated�previously�can�signal�Response�and�Result,�can�itself�be�signalled�by�these�finite�categories�of�grammatical�connectives.�
One�key�aspect�to�note�about�these�Vocabulary�1�and�2�items�is�their�inter-changeability,� in� certain� circumstances,� not� only� within� a� vocabulary� type� but�also�across�vocabulary�types.�Winter�(1971:�45)�cites�the�following�example�of�an�Instrument-Achievement�relation�to�show�the�syntactic�and�semantic�properties�of�so. In� the� sentences�below,� so� can�be� replaced�by� thus,� another�grammatical�item�from�the�same�Vocabulary�2�class.�However,�one�of�the�questions�that�still�needs�an�answer�is�under�what�circumstances�we�would�use�one�signal�rather�than�another�given�their�apparent�changeability.
� (3)� a.� The�hovercraft�terminals�can�be�sited�away�from�the�main�ports,�and�so � � � relieve�overcrowded�dock�systems.
� (3)� b.� The�hovercraft�terminals�can�be�sited�away�from�the�main�ports,�thus relieving�the�overcrowded�dock�systems.
Other� examples� of� the� Instrument–Achievement� relation� taken� from� Proctor�(1988:�25)�demonstrate�how�a�Vocabulary�1�item,�the�subordinator�by -ing,�can�be�substituted�by�the�Vocabulary�2�sentence�connector�Thus.
� � By�appealing�to�my�father’s�sense�of�humour,�I�avoided�upsetting�him�immedi-ately�when�I�told�him�that�his�car�had�been�stolen�outside�the�police�station.
� � I� appealed� to� my� father’s� sense� of� humour.� I� thus� avoided� upsetting� him�immediately�when�I�told�him�that�his�car�had�been�stolen�outside�the�police�station.
However,� replacement� of� one� item� with� another� is� not� always� possible,� as� the�choice�of�one�over�the�other�is�governed�by�the�context.�As�Proctor�(1988)�points�out�the�grammar�of�subordination�in�the�first�sentence�above�presents�the�infor-mation�of�its�clause�as�given�by�the�context�of�the�utterance,�whereas�the�gram-mar�in�the�second�sentence�presents�the�same�information�as�new.�Grammatical�choices�are�therefore�highly�dependent�upon�not�only�the�semantic�relations�ex-
� Chapter�1.� Problem-Solution�pattern� 5
isting�between�clauses�and�sentences,�but�also�pragmatic�factors�derived�from�the�context.�
So�far,�these�grammatical�items�have�been�discussed�in�terms�of�their�signal-ling�effectiveness�for�identifying�clause�relations,�but�as�Hoey�(1983)�points�out�our�starting�point�can�also�be�with�a�description�of�clause�relations�as�a�way�of�shedding�light�on�the�nature�of�these�devices.�He�also�notes�that�for�Winter�the�signal�and�relation�are�of�equal�importance,�with�each�requiring�a�description�of�the�other�for�identification.�Another�important�point�to�note�is�that�although�these�clause�relations� tend�to�be�realised�by�certain�grammatical� items,�by�no�means�is�there�a�one-to-one�correspondence�between�the�signal�and�its�clause�relation:�‘Texts�often�contain�strong�clues�or�signals�as�to�how�we�should�interpret�the�rela-tions�between�segments;�these�are�not�absolutely�deterministic�but�are�supporting evidence to�the�cognitive�activity�of�deducing�relations’�(McCarthy�1991:�29).
However,�attempts�have�been�made�to�provide�lists�of�grammatical�items�as�‘supporting� evidence’� for� identifying� the� Problem-Solution� pattern� by� Jordan�(1984)�and�Proctor�(1988).�Based�on�her�example�texts,�Proctor,�conflating�Win-ter’s�Vocabulary�1�and�2�items,�gives�a�list�of�grammatical�exponents�for�realising�each�of�the�four�basic�components�of�the�Problem-Solution�pattern.�Jordan’s�lists�are�somewhat�different�from�those�of�Proctor�as�he�does�not�discuss�Winter’s�vo-cabulary�1�and�2�items,�but�classes�both�grammatical�and�lexical�items�under�a�category�of�Signals of Logic.�Here,�some�of�the�grammatical�items�such�as�by …ing�and�so belong�in�Winter’s�Vocabulary�1� items�of�subordinators,�whereas�others�such�as�as a result and�therefore belong�to�his�Vocabulary�2�items�of�sentence�con-nectors.�Although�the�classification� lists�of� Jordan�and�Proctor�are�not�without�their�respective�merits,�an�inherent�weakness�with�both�of�them�is�that�they�do�not�consider�the�mediating�role�of�clause�relations�in�the�process:�‘…�supplying�connections�to�a�discourse�with�subordination�and�conjuncts�is�a�test�not�of�the�existence�of�the�Problem-Solution�pattern�but�of�the�existence�of�particular�rela-tionships�(i.e.�Cause–Consequence,�Instrument–Achievement)�holding�between�(normally)�adjacent�parts�of�a�discourse’�(Hoey�1983:�57).
Lexical signals of clause relations for the Problem-Solution pattern
The�picture�is�a�little�clearer�for�those�lexical�signals�of�the�Problem-Solution�pat-tern�as�there�exist�more�areas�of�agreement�among�researchers�as�to�what�consti-tutes�lexical�signals.�Although�Hoey�(1983)�mentions�that�lexical�signalling�can�take�the�form�of�a�sentence,�clause�or�phrase,�the�normal�procedure�is�to�focus�on� individual� lexical� items,� which� is� the� case� in� this� section.� Hoey’s� definition�(1983:�63)�emphasises�the�importance�of�their�role�in�the�encoding/decoding�of�
6� Corpus-based�Analyses�of�Problem-Solution�Pattern�
textual�meaning,�thus�underscoring�the�intentional�and�interpretative�nature�of�such�signals:� ‘Lexical�signals�are� the�author’s/speaker’s�explicit� signalling�of� the�intended�organisation�and�are� therefore�obviously�of�primary� importance;� it� is�probable�that�they�are�one�of�the�main�means�whereby�a�reader/listener�‘decodes’�a�discourse�correctly’.� Jordan�(1984:�4–5),�meanwhile,� suggests�specific� lexis� for�signalling�the�Problem-Solution�pattern:
Within�a�defined�situation,�you�will�recognise�a�‘problem’�in�the�widest�sense�of�the�word.�…words�that�indicate�this�concept�–�not�just�the�word�problem�itself,�but� its�near-synonyms�difficulty, dilemma, drawback, danger, snag, hazard,�and�so�on,�and�words�such�as�pest, unpleasant, disorganised, fear, smelly�and� illness.�Whenever�we�recognise�such�a�word�in�the�text,�we�expect�the�text�to�tell�us�of�a�solution�(actual,�attempted,�or�proposed),�and�solutions�are�recognised�as�things�or�actions�that�avoid, counteract,�reduce, prevent�or�overcome�the�problem.�Then�the�text�may�evaluate�the�effectiveness�of�the�solution�with�such�words�as�excel-lent, important, quick, unique�and�failure.
Lexical�signals�for�the�Problem-Solution�pattern�have�been�discussed�by�Winter�(1977)�under�the�rubric�of�‘Vocabulary�3’�items.�These�are�discourse-organising�words�which�can�also�replace�Vocabulary�1�or�2�items,�outlined�in�the�previous�sec-tion,�to�express�the�same�meaning.�To�take�an�example�from�McCarthy�(1991:�29),�the�Cause–Consequence�relationship�can�be�expressed�through�the�Vocabulary�3�item�reason, e.g.�‘The reason he went home was that he was feeling ill’�as�well�as�through�the�Vocabulary�1�item�because as�in�the�sentence�‘Because he felt ill, he went home’.�There�therefore�exists�a�choice�between�a�lexical�(i.e.�Vocabulary�3)�or�a�grammatical�item,�Vocabulary�1�in�the�case�above,�just�as�there�exists�a�choice�between�different�grammatical� items�within�Vocabulary�1,�as�mentioned�previ-ously.�However,�under�what�conditions�one�grammatical�item�would�be�preferred�over�another,�or�a�lexical�item�preferred�over�a�grammatical�item�to�convey�the�same�clause�relation,�is�obviously�dependent�on�certain�pragmatic�and�contextual�features�of�the�discourse.
To�illustrate�how�these�various�Vocabulary�3�items�operate�in�text�as�signals�for� the� Problem-Solution� pattern,� let� us� examine� the� following� example� from�Harris�(1986:�163).
� S3� On�October�9th�Henry�set�off�for�Calais,�leaving�half�of�his�arms�at�Harfleur�and�taking�the�other�half�with�him.
� S4� It�had�been�raining�heavily�in�the�last�few�days�and�all�the�rivers�were�swol-len.
� S5� Henry�found�it�very�difficult�to�cross�the�fords�and�rivers�as�the�French�army�always�ran�parallel�and�protected�each�fording�place.
� Chapter�1.� Problem-Solution�pattern� 7
� S6� Henry�solved this problem by�cutting�very�quickly�across�a�neck�of�the�land�before�the�French�could�and�he�managed to�get�across.�(H.2.A.11)
In�the�above�example,�the�lexis�difficult, solved this problem�and�managed�all�func-tion�as�signals�for�various�elements�of�the�Problem-Solution�pattern,�but�act�as�signals� in� different� ways.� Solved� and� problem� clearly� have� a� discourse-organis-ing�role:�the�item�this problem�refers�retrospectively�to�the�fact�that�it�was�‘very�difficult to�cross�the�fords�and�rivers’�and�solved sets�up�an�anticipated�solution.�However,�it�should�be�pointed�out�that�whether�a�noun�such�as�problem�functions�anaphorically�is�dependent�on�its�accompanying�deictic.�In�the�phrase�This prob-lem,�it�is�the�demonstrative�This which�carries�the�burden�of�anaphoric�reference.�Here,�problem�has�the�function�of�what�is�being�referred�to.�
The�items�difficult and�managed, while�not�signalling�the�overall�text�organisa-tion,�still�operate�as�lexical�signals�for�Problem�and�Evaluation�respectively,�acting�as�the�referential�vocabulary�for�these�elements,�and�thus�play�a�more�local�role�in�creating�textual�coherence.�Obviously,�the�same�lexical�item�can�operate�either�as�a�referring�(discourse-organising)�or�referential�(discourse)�signal�depending�on�other�contextual�features�of�the�discourse.�For�instance,�in�the�example�supplied�above,�in�S5 we�could�paraphrase�‘…�very�difficult to�cross�the�fords�and�rivers’�as�‘a�problem to�cross�the�fords�and�rivers’.�In�this�case,�the�item�problem�would�be�acting�as�a�local�discourse�signal�rather�than�a�connective�one,�binding�adjacent�clauses�and�sentences,�as�in�S6 above.�
It�is�also�worthwhile�to�mention�here�the�other�terms�used�in�the�literature,�besides�Vocabulary�3�items,�to�designate�those�types�of�nouns�which�have�a�meta-discursive�i.e.�discourse-organising�function�and�rely�on�the�context�for�their�full�interpretation.� Francis� (1986,� 1994)� refers� to� ‘anaphoric� nouns’,� Ivanič� (1991)�talks�of�‘carrier�nouns’�and�Schmidt�(2000)�of�‘shell�nouns’�–�see�Schmidt�(2000,�Chapter� 2)� for� a� helpful� review� of� these� overlapping� categories.� More� recently,�Flowerdew’s�(2003a,�2003b,�2006)�corpus-based�research�on�signalling�nouns�re-veals�the�key�discourse�role�such�types�of�abstract�nouns�play�in�establishing�links�across�and�within�clauses.
As�regards�the�Problem-Solution�pattern,�both�Jordan�and�Proctor�have�sup-plied�useful�sets�of�lexis�realizing�different�elements�of�the�pattern;�however,�the�drawback�of�both�of�these�lists�is�that�they�are�based�on�a�limited�number�of�texts.�Proctor’s� analysis� is� based� on� only� four� academic� texts� in� the� fields� of� Science�and�Technology�while� Jordan’s� list� is�derived� from�a�somewhat� random�choice�of�various�text�segments�covering�different�genres�and�registers.�Proctor,�writing�presciently�in�1988,�notes�that�such�analysis�for�the�identification�of�lexical�signals�could�very�usefully�be�aided�by�computational�techniques:�
8� Corpus-based�Analyses�of�Problem-Solution�Pattern�
The� work� of� compiling� an� index� of� discourse� signals� that� could� eventually� be�incorporated� in� the�contextual�grammar�of�English,� though� lengthy�and�time-consuming,�is�not�impossible.�Recent�advances�in�information�technology�have�greatly�facilitated�statistical�counts�and�storage.�It�is�possible�that�certain�word�or�phrase�locating�programs�can�be�used�to�speed�up�parts�of�the�analysis.�Indeed,�the�development�of�computational�techniques�for�this�kind�of�analysis�may�pres-ent�challenging�and�rewarding�lines�of�enquiry�for�interested�individuals.�� (Proctor�1988:�42)
The�following�two�sections�give�a�taste�of�how�a�grammatical�and�lexical�signal�for�the�Problem�element�can�be�fruitfully�analysed�from�a�corpus�analytic�perspective�based�on�the�phraseological�approach�to�language.�
Corpus analysis of a grammatical signal for the Problem element
One�key�grammatical�item�that�has�been�frequently�mentioned�as�a�signal�for�the�Problem-Solution�pattern� is� the�connector�however.�This� item�was�searched�in�a�corpus�of�professional�environmental�reports�(PROFCORP)�of�approximately�225,000�words�comprising�60�executive� summaries,�one�of� the� two� specialized�corpora�under�discussion�in�this�book�(see�Chapter�3�for�a�description�of�this�cor-pus).�Out�of�a�total�of�8,724�types�(the�number�of�different�word�forms),�however was�found�to�be�the�100th�most�frequent�with�264�tokens.�In�spite�of�its�high�fre-quency,�it�did�not�show�up�as�a�key�word,�i.e.�a�word�of�unusually�high�frequency�when�compared�with�a�large-scale�general�reference�corpus�(Scott�1997,�2001a)�in�this�case,�the�100-million�word�British�National�Corpus,�BNC.�This�may�well�be�because�however�is�used�not�only�in�the�technical�genre�of�report�writing�but�also�in�everyday�English�as�providing�evidence�for�the�Problem-Solution�pattern.�
It�would�be�of�interest�to�examine�how�this�item�functions�from�a�phraseolog-ical�perspective,�i.e.�to�have�a�look�at�its�colligational�and�lexico-grammatical�pat-terning�and�how�it�relates�to�different�elements�of�the�Problem-Solution�pattern.�
Colligation,� a� phenomenon� first� described� by� Firth� (1957),� refers� to� ‘the�grammatical�company�a�word�keeps’�(Hoey�1997:�8).�For�example,�Hoey�(1993),�using�a�corpus�of�just�under�100�million�words,�demonstrates�how�reason�has�a�colligational�relationship�only�with�the�demonstrative�deictics�and�not�with�the�possessive�ones.�Colligation�also�refers�to�the�positioning�of�a�word�in�a�sentence,�another�concept�which�has�been�variously�defined.�Francis�(1991)�takes�this� to�mean�the�distribution�of�a�word�across�subject,�object�and�complement�slots�in�a�sentence,�whereas�Hoey�also�considers�this�term�from�the�Hallidayan�perspective�of�Theme�/�Rheme�position.�In�my�specialized�corpus,�however was�found�to�have�
� Chapter�1.� Problem-Solution�pattern� 9
a�colligational�preference� for� sentence-initial�position�with�184,� i.e.�70%�of� the�tokens�occurring�in�this�position.�
As� expected,� most� of� the� instances� of� however introduced� problems� that�might�or�might�not�arise�from�the�proposed�construction�activities,�with�reasons�given.�
� � However, water quality impacts may arise due to contaminated runoff from the construction sites.
However, as the proposed road improvement scheme is well away from the sea, there would not be any direct discharge of effluent to sea waters.
Three�other�patterns�for�however�were�also�discernable.�First,�it�was�used�to�indi-cate�that�the�solution�was�only�a�partial�one,�and�that�an�aspect�of�the�problem�still�remained�(an�example�of�‘multilayering’),�as�in�Table�1-1�below.
In�the�other�two�patterns�however was�used�as�a�linking�device,�binding�the�Problem� and� Solution� elements.� The� structure� in� Table� 1-3� (however, with� +�nominalization)�was�found�to�occur�in�concluding-type�sentences�where�the�pro-posed�solution�had�already�been�discussed�earlier�in�the�report.�
Table 1-1. Concordance�for�however�to�indicate�a�partial�solution
Site�were�used�to�accommodate�the�car�park.� However these�are�insufficient�to�allow�full�coul�design�of�flood�lighting�to�minimise�glare. However the�effectiveness�of�these�measurng�Lap�Kok�eastern�shore�will�remain�intact. However, the�revised�configuration�will�alsoecked�over�so�that�the�noise�will�be�enclosed. However, these�new�roads�will�attract�addit
Table 1-2. Concordance�for�however�to�signal�solution�to�problem
or�disposal�to�a�non-containment�landfill�site. However,� filtered�dust�could�be�landfilled�at�Se�of�the�acceptable�noise�levels�are�exceeded. However 3�dBA�should�be�added�to�the�preishing�activities�in�the�Western�harbour�will, however, be�progressively�curtailed�in�the�ating�vegetation�will�result�in�visual�intrusion however, these�slopes�will�be�planted�and�the�d
ondary�schools�near�the�road.�The�impact�can� however be�minimized�by�appropriate�mitigati
Table 1-3. Concordance�for�however�to�signal�solution�to�problem
mended�TSP�hourly�guideline�of�500�ug/m3. However,� with�the�implementation�of�standard�in�noise�levels�similar�to�ambient�conditions.� However, with�the�provision�of�suitable�site��itive�receptors�at�the�Lung�Kwu�Tan�villages. However, with�the�provision�of��appropriate�
concentrations�may�exceed�acceptable�limits.� However, with�the�adoption�of�dust�supressionsuspended�particles�lost�from�surface�soils. However, with�the�use�of�hard�surfaces�and�an
10� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Corpus analysis of a lexical signal for the Problem element
Pollution was�found�to�occur�220�times�in�PROFCORP.�Although�this�item�was�less�frequent�than�however (264�occurrences),�it�was�found�to�be�key�in�the�corpus�as�a�whole,�whereas�however was�not.�When�each�individual�report�was�compared�with�the�larger-scale�reference�corpus,�pollution�was�also�found�to�be�key�in�10�out�of�the�60�reports�(see�Flowerdew�2003�for�further�details�on�keyword�analy-sis).�These�results�show�that�the�keyness�of�a�word�may�not�necessarily�be�related�to�frequency,�in�cases�where�the�word�reflects�the�topic�of�a�specialized�genre.
Pollution�was�also�found�to�have�certain�collocational�preferences�and�pat-terning.�It�should�be�noted�that,� like�colligation,�collocation�has�been�variously�defined.�Whereas�Sinclair�(1987)�and�McEnery�and�Wilson�(2001)�relate�colloca-tion�to�measures�of�statistical�significance,�i.e.�considering�lexical�items�with�items�that� appear� with� greater� than� random� frequency,� Cowie� and� Howarth’s� (1996)��approach�is�to�favour�the�“textual”�over�the�“statistical”�identification�of�collocates�for�the�following�reasons:
Collocations�are�often�described�as�fixed�and� recurrent�word-combinations….�But�both�parts�of�this�description�are�misleading.�Typically,�collocations�are�not�fixed�but�variable�to�a�limited�and�arbitrary�degree.�As�for�frequency,�it�can�be�shown� that� individual restricted collocations may recur to only a limited extent within a given text or across several texts devoted to the same topic�[my�italics].�It�is�best�to�think�of�a�collocation�as�a�familiar�(institutionalized),�stored�(memorized)�word-combination�with�limited�and�arbitrary�variation.� (Cowie�&�Howarth�1996:�82)
Likewise,�Stubbs�(2001c:�74–75)�puts�forward�a�similar�reason�as�to�why�measures�of�statistical�significance�may�be�of�limited�use�in�some�cases.�He�cites�the�example�of�a�small�corpus�yielding�the�following�data�for�the�node�adverb�‘distinctly’:
–� <distinctly�<N�+�1:�cagey,�cool,�dated,�dour,�downbeat,�iffy,�inferior,�meaner,�muted,�strange,�thin,�unimpressed,�unwell>
Stubbs�remarks�that�the�above�adjectival�collocates�occurred�only�once�each�and�therefore�statistical�measures�to�determine�the�likelihood�of�co-occurrence�could�not�be�carried�out.�What�he�does�pinpoint�in�these�data,�is�the�attraction�of�‘dis-tinctly’�with�disapproving�words,�thus�emphasizing�the� interpretation�of�corpus�findings�by�the�human�analyst.�This�is�the�approach�I�have�tended�towards�in�this�book�–�interpretation�of�small�corpus�data�by�the�human�analyst,�not�only�inter-pretation�of�the�text�internally�at�the�lexico-grammatical�level,�but�also�externally�with�recourse�to�contextual�and�situational�features�of�the�discourse.
� Chapter�1.� Problem-Solution�pattern� 11
To�return�to�the�analysis�of�problem,�I�examine�its�collocations�from�a�“tex-tual”�perspective�as�certain�collocations�would�show�up�‘to�only�a�limited�extent’�in� this�specialized�corpus.�As� for� the�collocational�preferences�of�pollution,�air�and�water�were�the�most�common,�co-occurring�39�and�27�times�with�pollution,�respectively.�Pollution also�occurred�14�times�in�what�appeared�to�be�a�semi-fixed�phrase� allowing� some� lexical� variation:� environmental protection and pollution control measures / requirements.
Pollution typically�occurred�in�two�main�phraseologies.�In�the�first�pattern�pollution�was�followed�by�from, a�reduced�form�of arising from which�was�some-times�used�instead,�and�thus�involved�a�cause-consequence�relationship�as�shown�by�the�examples�in�Table�1-4.
Pollution was�also�found�in�means-purpose�clauses,�with�‘two-way’�signaling�verbs,�such�as�‘reduce’,�‘prevent’�and,�in�particular,�‘minimize’.�Such�verbs�are�an-other�means�whereby�the�Problem�and�Solution�elements�are�linked�(Table�1-5).�
The�sample�concordance�lines�above�of�the�grammatical�signal,�however,�and�the�lexical�signal,�pollution,�thus�exemplify�the�value�of�concordancing�techniques�to�reveal�common�phraseologies,�which�may�not�be�retrievable�solely�through�in-tuitive�means.�
Conclusion
This�introductory�chapter�has�laid�out�the�theoretical�groundwork�for�the�means�of� identifying� the� Problem-Solution� pattern� in� text� through� a� clause� relational�approach�to�text�analysis.�It�has�also�discussed�the�lexical�and�grammatical�sig-nals�for�identifying�the�Problem�and�Solution�elements�in�various�clause�relations.�
Table 1-4. Concordance�of�pollution�followed�by�‘from’
ll�be�dust�emissions�from�site�formations.�Air pollution from�site�and�motor�vehicles�are�likelSimilarly�means�to�reduce�the�potential�for pollution from�fuel�spillage�on�site�have�be�sugthe�use�of�silt/oil�traps�will�prevent�marine pollution from�on-site�construction�activities�at
llowing�development�may�result�from�traffic pollution from�the�new�road�network.�Pollutanpended�solid�matter�in�site�run-off�or�organic pollution from�foul�effluent.�As�the�scale�of�th
Table 1-5. Concordance�of�pollution�in�means-purpose�clauses
tion�phases�of�the�development.�To�minimize� pollution and�nuisance�from�the�developmenttrmwater.�All�possible�measures�to�minimise pollution loads�should�be�implemented�and
Works�to�the�Urmston�Road;�to�minimise pollution, environmental�and�ecological�disturboutside�the�embayment�area�to�reduce�the pollution. Loading�into�the�trapped�body�of�wat
the�south�of�the�western�seawall.�To�prevent pollution of�marine�waters�by�floating�debris
12� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Most�importantly,�corpus-based�techniques�have�been�shown�to�be�very�useful�for�identifying� the�phraseologies�of� such�signals� for� the�Problem-Solution�pattern.�This�approach�therefore�adopts�a�more�discourse�analytic�perspective�to�corpus�linguistics,�an�issue�that�is�taken�up�in�detail�in�the�following�chapter.
chapter�2
Issues in corpus linguistics and discourse studies
This�chapter�addresses�several�key�issues�in�corpus�linguistics�and�discourse�anal-ysis�which�are�pertinent�to�the�major�themes�of�this�book,�namely,�the�method-ologies�employed,�contextual�features,�and�interpretation�of�data.�Here�I�make�the�case� that�by� taking�a�more�discourse�analytic�approach� to�corpus-based� inves-tigations,�some�of�these�issues�can,�to�a�certain�extent,�be�resolved.�At�the�same�time,�corpus-based�approaches�also�have�advantages�for�discourse�analysis�(see�Baker�2006:�10–17,�for�a�succinct�account�of�the�advantages�of�the�corpus-based�approach�to�discourse�analysis).�
McEnery�et�al.� (2006)�offer� the� following�dichotomies�of�corpus� linguistics�vis-à-vis�discourse�analysis:
…while�DA�emphasizes�the�integrity�of�text,�corpus�linguistics�tends�to�use�rep-resentative� samples;�while�DA� is�primarily�qualitative,� corpus� linguistics� is� es-sentially�quantitative;�while�DA�focuses�on�the�contents�expressed�by�language,�corpus�linguistics�is�interested�in�language�per se;�while�the�collector,�transcriber�and�analyst�are�often�the�same�person�in�DA,�this�is�rarely�the�case�in�corpus�lin-guistics…� (McEnery�et�al.�2006:�111)
In�other�words,�the�strengths�of�corpus�linguistics�tend�to�be�the�weaknesses�of�discourse�analysis,�and�vice-versa.��With�reference�to�the�quotation�above�‘while�DA� focuses� on� the� contents� expressed� by� language,� corpus� linguistics� is� inter-ested�in�language�per se’,�both�approaches�to�text�analysis�could�be�considerably�strengthened� if,� for� example,� the� phraseologies� uncovered� through� corpus� lin-guistics�techniques�could�fruitfully�inform�genre�analysis,�while�genre�analytic�ap-proaches�could�be�applied�to�corpus-based�analyses�to�shed�light�on�the�rhetorical�aspects�of�text�organization.�This�point�is�taken�up�in�more�detail�in�the�follow-ing�section�on�methodologies�(see�Biber�et�al.�2007�for�studies�which�use�corpus�analysis�to�describe�genre�moves).
14� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Methodologies
Swales�(2002,�2004)�has�commented�on�the�methodologies�that�corpus�linguistics�employs�(assuming�that�one�accepts�the�basic�premise�that�corpus�linguistics�is�a�methodology�rather�than�a�theory�of�language;�see�McEnery�et�al.�2006:�7–8�for�a�review�of�this�argument).�Swales�argues�that�the�software�commonly�used,�namely�concordancing�packages�for�displaying�the�key-word-in-context,�constrains�anal-ysis�to�a�somewhat�atomized,�bottom-up�type�of�investigation�of�the�corpus�data.�This�type�of�analysis�is�considered�to�be�at�odds�with�the�more�top-down�kind�of�process-based�analysis�associated�with�the�genre�approach�to�text�analysis,�where�the�starting�point� is�with� the�macrostructure�of� the� text�with�a� focus�on� larger�units�of�text�rather�than�sentence-level,�lexico-grammatical�patterning.�Parting-ton�(1998)�has�called�for�a�‘symbiosis’�of�these�top-down�and�bottom-up�strate-gies,�which�is�evident�in�several�recent�corpus-based�studies�making�use�of�the�move�structures�of�genre�analysis�(see�Bhatia�et�al.�2004;�Connor�et�al.�2002;�Flow-erdew�2008b).�For�example,�Bhatia�et�al.�(2004)�examined�some�common�verbs�with�their�noun�collocations�in�the�four�prototypical�move�structures�in�law�cases�(see�Table�2-1).�Bhatia�et�al.�found�that�verbs�had�a�clear�preference�for�certain�move�structures,�with�submit� in� the�move�presenting argument�often�appearing�in�the�patterning�“counsel�for�the�plaintiff/defendant�submitted�that…”,�or�“it�was�submitted�that…”.��This�more�quantitative�approach�of�corpus�linguistics�can�thus�augment�the�more�qualitative-based�analyses�of�genre�approaches.�
This�more�genre�analytic�approach�to�corpus�analysis�counteracts�to�some�ex-tent�the�following�criticism�made�by�Grabe�and�Kaplan�(1996),�who�raised�queries�regarding�corpus�research�on�the�grounds�that�the�field�lacks�a�theoretical�founda-tion�for�the�interpretation�of�data,�thus�implying�that�its�methodological�basis�is�somewhat�open�to�question.
Table 2-1. Position�of�noun-verb�collocations�in�law�cases�(Bhatia�et�al.�2004:�214)
Genre Move FrequencySubmit Dismiss Reject Grant
1��Facts�/�Stating�history�of�the�case � 75 � 47 12 � 822��Presenting�argument 263 � � 6 � 9 � 513��Deriving�ratio�decidendi � � 5 � 16 44 � 804��Pronouncing�judgment � � 3 � 42 � 9 � 16
Total 346 111 74 229
� Chapter�2.� Issues�in�corpus�linguistics�and�discourse�studies� 15
The� general� dilemma� facing� most� projects� on� corpus� research� is� the� lack� of� a�theoretical�foundation�for�the�interpretation�of�the�results�prior�to�the�analysis.�Thus,�most� corpus� research�has�been�of� a�post-hoc�nature,� looking�at� the� fre-quency�counts�and�deciding�what�can�be�said�about�these�results.� (Grabe�&�Kaplan�1996:�46)
However,�to�date,�this�integration�of�corpus�and�genre�approaches�has�been�utilized�only�for�those�genres�which�exhibit�a�fairly�formulaic,�conventionalized�rhetorical�structure�such�as�job�application�letters�and�law�cases�and�for�small�corpora,�as�the�data�would�have�to�be�examined�and�coded�manually�for�identification�of�move�structures�(Flowerdew�2005).�Those�texts�comprising�mixed�genres�or�consisting�of�embedding�of�move�structures�would�present�a�challenge�for�existing�software,�although�software�tools�are�becoming�increasingly�sophisticated�and�a�tool�such�as�WinMax has�the�flexibility�to�code�embedded�move�structures.�
Contextual features
Another�main�argument�that�has�been�put�forward�against�a�corpus-based�meth-odology�for�analysis�of�text�is�that�it�does�not�take�account�of�the�contextual�fea-tures�of�text.�As�Widdowson�(1998,�2002)�points�out,�corpus�data�are�but�a�sample�of� language,� as� opposed� to� an� example� of� authentic� language,� because� it� is� di-vorced�from�the�communicative�context�in�which�it�was�created:�‘the�text�travels�but� the� context� does� not� travel� with� it’.� In� this� respect,� Tribble� (2002)� outlines�an�analytic�framework�for�contextual�analysis�derived�from�a�genre�analytic�per-spective,�which�he�views�as�crucial�for�informing�corpus-based�analyses.�Tribble’s�position,�then,�is�to�see�the�role�of�context�as�very�much�informing�corpus-based�analyses.
Although�the�above�features�are�really�only�a�skeleton�of�the�intricate,�multi-dimensional� contextual� network� expounded� in� recent� genre� studies� (Bhatia�2004),� even� such� rudimentary� and� essential� contextual� aspects� are� not� usually�taken�into�account�in�corpus�investigations�as�the�analyst�does�not�have�recourse�to�the�communicative�context� in�which�the�text�was�produced.�However,�more�recently,�spoken�corpora�such�as�the�Michigan�Corpus�of�Academic�Spoken�Eng-lish�(Simpson-Vlach�&�Leicher�2006)�have�been�marked�up�for�speech�events�and�speaker�attributes,�thus�allowing�a�more�context-sensitive�analysis�of�the�data.
It�is�this�absence�of�context�that�poses�one�of�the�most�serious�drawbacks�for�the�interpretation�of�concordance�lines�(Hunston�2002),�another�aspect�of�corpus�linguistics�that�has�been�much�debated�in�the�literature.�
16� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Interpretation of data
This� lack� of� knowledge� of� contextual� features� and� social� practices� can� be� par-ticularly�problematic�for�the�corpus�analyst�when�dealing�with�pragmatic�features�of�text,�which�may�only�be�recoverable�form�the�socio-cultural�context�or�other�features�of�the�text,�as�noted�by�Widdowson:
…on�the�evidence�of�their�customary�collocates,�particular�words�can�be�shown�to�have�a�typical�positive�or�negative�semantic�prosody,�and�it�can�be�plausibly�suggested� that� facts� of� co-textual� co-occurrence� should� be� recognized� as� part�of�the�semantic�signification�of�such�words.�But�this,�of�course,�does�not�tell�us�about�what�pragmatic significance� [my� italics]�might�be�assigned� to�such�a�co-occurrence�in�a�particular�text.�The�point�about�these�co-textual�findings�is�that�they� are� a� function� of� analysis,� with� texts� necessarily� reduced� to� concordance�lines.�One�might�trace�a�particular�line�back�to�its�text�of�origin,�but�then�if�it�is�to�be�interpreted,�it�has�to�be�related�to�other�features�of�the�original�text.� (Widdowson�2004:�60)
However,�in�a�paper�on�corpus�semantics�Stubbs�(2001a)�argues�that�the�conven-tionalized�view�that�pragmatic�meanings�are�usually� inferred�by� the�reader/lis-tener,�making�them�highly�context-dependent,�may�be�overstated�and�that�large-scale�corpus�studies�can�provide�evidence�to�show�that�pragmatic�meanings,�like�semantic�prosodies,�can�also�be�conventionally�encoded�in�linguistic�form.�This,�though,�may�depend�on�the�type�of�corpus�under�investigation�and�whether�one�has�knowledge�of�the�discursive�conventions�of�the�genre.�In�this�respect,�Bhatia�et�al.�(2004)�point�out�that�the�two�verbs�dismiss�and�reject�used�in�law�cases�(see�
Table 2-2. Analytic�framework�(Contextual)�(Tribble�2002:�133)
Contextual analysis
1. name What�is�the�name�of�the�genre�of�which�this�text�is�an�exemplar?2. social context In� what� social� setting� is� this� kind� of� text� typically� produced?�
What� constraints� and� obligations� does� this� setting� impose� on�writers�and�readers?
3. communicative purpose What�is�the�communicative�purpose�of�this�text?4. roles What�roles�may�be�required�of�writers�and�readers�in�this�genre?5. cultural values What�shared�cultural�values�may�be�required�of�writers�and�read-
ers�in�this�genre?6. text context What�knowledge�of�other�texts�may�be�required�of�writers�and�
readers�in�this�genre?7. formal text features What�shared�knowledge�of�formal�text�features�(conventions)�is�
required�to�write��effectively�in�this�genre?
� Chapter�2.� Issues�in�corpus�linguistics�and�discourse�studies� 17
Table�2-1)�appear�to�be�almost�synonymous�semantically,�but�that�if�one�wanted�to�make�a�pragmatic�distinction�between�them,�it�would�be�necessary�to�look�at�the�institutional�and�discursive�practices�of�this�genre.
Evidence�in�support�of�Stubbs’�view�is�provided�by�O’Halloran�and�Coffin’s�(2004)�research�motivated�by�critical�discourse�analysis�(CDA)�approaches.�Based�on�a�45-million-word�sub-corpus�of�the�Sun newspaper�drawn�from�the�450-mil-lion-word�Bank�of�English,�O’Halloran�and�Coffin�show�how�negative�attitudinal�meaning� can� be� gleaned� from� multiple� concordance� lines;� an� accumulation� of�negative�co-texts�for�United States of Europe�displays�a�regular�negative�attitude�for� ‘United� States� of� Europe’,� thus� reflecting� the� anti-Europe� stance� of� the� Sun�newspaper.�Such�an�ideological�stance�may�not�be�immediately�obvious�when�en-countered�as�a�single�instance,�but�can�be�retrieved�from�examining�the�co-textual�environment�of�repeated�occurrences�of�the�search�word�in�a�large�corpus.
CDA�approaches�to�text�analysis�often�employ�various�categories�from�Halli-day’s�systemic�functional�grammar�(1994),�most�notably�the�aspects�of�transitivity�and�nominalization.�In�their�research,�O’Halloran�and�Coffin�made�use�of�experi-ential�meanings�to�uncover�negative�stance.�Using�concordancing�techniques�they�uncover�a�pattern�where�Brussels�or�the�EU�are�primary�‘doers’,�and�when�the�EU�is�the�implicit�Initiator,�Britain�is�an�Actor�carrying�out�an�activity�initiated�by�the�EU:�‘The�continual�reinforcement�of�this�pattern�helps�to�establish�the�experien-tial�meaning�in�the�text�of�Britain�as�powerless�in�the�face�of�the�EU’�(p.�283).�The�following�examples�illustrate�this�stance.
� � …Brussels�aimed�to�snatch�power�over�UK�employment,�foreign�affairs…� � Actor� � � � � � � � [� � � Goal� � � � ]
� � …Britain�would�be�forced�to�surrender�control�of�its�economy�to�Brussels�[by�the�EU]
� � Actor� � � � � � � � [� Goal� � � ]� implicit�ini-tiator� ]
Table 2-3. Concordance�lines�for�‘United�States�of�Europe’�(adapted�from�O’Halloran�&�Coffin�2004:�288)
leader’s�bleak�plan�for�a�� United�States�of�Europe came�as�a�hammer�blow�tothe�road�towards�a�Federal United�States�of�Europe. Hague�has�never�tried�to
forming�into�a�giant� United�States�of�Europe –�with�the�same�tax�andThe�empire�builders�want�a� United�States�of�Europe. Thank�goodness�you�have
thirds�say�there�will�be�a United�States�of�Europe within�the�next�20�years.for�a�hopeless�dream�of�a�� United�States�of�Europe. He�is�certain�to�pay�the
was�the�first�step�to�a� United�States�of�Europe –�which�would�costor�a�state�in�a�newly-formed� United�States�of�Europe? These�are�the�central
Just�as�many�are�against United�States�of�Europe under�a�federal
18� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Here,�in�contrast�to�Tribble’s�viewpoint�that�contextual�features�of�a�genre�analytic�approach�inform�the�corpus�analysis,�we�have�another�perspective�from�critical�discourse� analysis� (CDA)� whereby� corpus� data� is� viewed� as� shedding� light� on�the�social�and�cultural�context�from�which�the�corpus�is�extracted,�as�exempli-fied�by�Table�2-3�and�the�examples�above.�This�is�in�line�with�Halliday’s�system�of�language�as�a�social�semiotic,�which�CDA�leans�heavily�on.�The�repetitions�of�linguistic�patterns�in�the�co-text,�revealed�by�co-selection�of�items�on�the�vertical�axis�of�the�concordance�lines,�reflect�the�context,�i.e.�the�situational�and�cultural�parameters�involved�in�the�creation�of�meaning�(Tognini-Bonelli�2001,�2004).�As�Blommaert�(2005:�66)�notes:�‘We�should�be�looking�at�how�the�linguistic�generates�the�economic,�social,�political,�as�well�as�how�the�economic,�social,�and�political�generate�the�linguistic’.�One�could�say�that,�generally�speaking,�a�CDA�approach�to�corpus�analysis�achieves�the�first�goal,�while�a�genre-analytic�approach�meets�the� second� of� Blommaert’s� aims.� But� even� the� traditional� distinctions� between�CDA�and�genre�(in� the�Swalesian�sense)�are�becoming�blurred�with� the�recent�work�of�Bhatia�proposing�the�need�for�a�critical�genre�analytic�approach�to�the�un-derstanding�of�discursive�practices,�which�rely�on�the�bending�of�generic�norms�to�present�a�certain�ideological�positioning�(see�Bhatia�2008�for�a�critical�analysis�of�a�corpus�of�corporate�letters�to�shareholders).
It�should�be�noted�that�the�field�of�CDA,�which�in�general�does�not�make�use�of�corpus�linguistic�techniques,�has�been�singled�out�for�its�cognitive�biases,�i.e.�‘reading�too�much’�into�individual�texts�and�assigning�ideological�significance�to�co-textual�relations�on�very�scant�evidence�and�pure�conjecturing�(cf.�Blommaert�2005;�Widdowson�2004).�Widdowson�takes�Fairclough�(1995)�to�task�for�assign-ing�the�co-occurrence�of�‘flock’�and�‘people’�a�passive�signification�only�through�an� intuitive� inclination� for� linking� ‘flock’� with� ‘sheep’.� Widdowson� (2004:�110)�concludes� that:� ‘CDA�might�more�profitably�draw�on�an�approach�to� linguistic�description�that�deals�with�texts�in�their�entirety�and�takes�explicit�account�of�co-textual�relations.�Corpus�analysis� is� just�such�an�approach…’;�the�corpus-based�CDA�research�of�O’Halloran�and�Coffin�bears�out�this�statement�and�points�the�way�for�future�interdisciplinary�research.�It�is�interesting�to�note�that�in�a�major�textbook�on�the�methods�of�CDA�(Titscher�et�al.�2000),�there�are�scant�references�to�‘corpus’,�which�does�not�even�appear�in�the�index.�
However,�co-occurrence�of�items�in�recurring�concordance�lines�still�has�to�be� interpreted� and,� as� Baker� (2006)� points� out,� a� potential� problem� lies� in� the�interpretation�being�open�to�contestation.�By�way�of�example,�Baker�(2006:�18)�cites�a�study�by�Rayson�et�al.�(1997)�which�found�that�people�from�socially�dis-advantaged�groups�used�more�non-standard�language�(e.g.�ain’t)�and�taboo�terms�(e.g.�bloody)�than�people�from�more�advantaged�groups.�Baker�notes�that:�‘while�the�results�aren’t�open�to�negotiation,�the�reasons�behind�them�are’,�commenting�
� Chapter�2.� Issues�in�corpus�linguistics�and�discourse�studies� 19
that�the�analyst�could�arrive�at�a�number�of�conclusions�to�explain�the�data�(e.g.�upbringing,�using�the�terms�to�show�group�identity�and�solidarity),�based�on�their�own�biases�and�identities.�
A�check�against�potential�misinterpretation�would�be�to�validate�one’s�inter-pretation�with�‘specialist�informants’�who�are�members�of�a�particular�discourse�community� familiar� with� its� discursive� practices.� Hyland� (1998),� for� example,�consulted�four�native-speaker�biologists�on�the�use�of�hedging�devices�in�a�corpus�of�80�research�articles�in�cell�and�molecular�biology.�He�asked�them�to�voice�their�reactions� to� underlined� features� in� the� text� and� had� them� participate� in� small�focus�group�discussions�to�elucidate�why�the�‘expert’�writing�under�investigation�was� appropriately� phrased� for� readers.� This� more� ethnographic� dimension� to�genre,�involving�data-gathering�procedures�such�as�participant�observation�and�input� from� subject� specialists,� is� usually� associated� with� the� New� Rhetoric� ap-proach�to�genre�studies,�where�the�focus�is�very�much�on�looking�at�how�various�aspects�of�the�socio-cultural�dimension�shape�the�genre.�
Corpus linguistics: Towards a multi-faceted approach
Chapter�1�laid�out�the�theoretical�underpinning�of�the�Problem-Solution�pattern�and�illustrated�via�selected�concordance�lines�for�the�items�however�and�pollution�how�a�more�discourse-analytic�perspective,�drawing�on�aspects�of�the�Problem-Solution� pattern,� could� inform� the� field� of� corpus� linguistics.� This� chapter� has�illustrated�how�other�areas�of�discourse�studies,�namely�genre�and�CDA,�which�view� text�as� socially-situated,�can�enhance� the�field�of�corpus� linguistics,� espe-cially�with�regard�to�contextual�issues�and�interpretation�of�the�data.�At�the�same�time,�these�three�approaches�to�text�analysis�can�profit�from�corpus�methodolo-gies�which�provide�quantitative�data�in�the�form�of� �recurring�phraseologies�as�evidence�for�different�elements�of�the�Problem-Solution�pattern,�certain�ideologi-cal�stances�in�CDA�or�prototypical�move�structures�in�genre�studies.
Based�on�the�lacunae�identified�between�corpus�linguistics�and�various�sub-fields�of�discourse�analysis�(cf.�Flowerdew�1998a)�this�book�aims�to�suggest�how�more�of�a�symbiosis�between�these�interdisciplinary�fields�can�be�achieved.��Spe-cifically,�this�book�will�deal�in�detail�with�the�following�question,�which�is�one�of�the�main�foci�of�the�book:
– How�can�elements�of�the�Problem-Solution�pattern�be�identified�through�cor-pus�linguistic�methodologies?
20� Corpus-based�Analyses�of�Problem-Solution�Pattern�
In�more�general�terms,�the�book�will�also�consider�the�following�aspects�in�the�phraseological� analysis� of� elements� for� the� Problem-Solution� pattern,� briefly�overviewed�in�Chapter�1.
– What�aspects�of�Halliday’s� systemic-functional�grammar�may�be�useful� for�analysis�of�the�corpus�data?�
– How�can�insights�from�genre�analysis�aid�interpretation�of�the�data?– What�can�recurring�patterns�in�the�data�tell�us�about�the�discursive�practices�
of�the�genre?
This� book� presents� a� small-scale� research� study� which� seeks� to� apply� insights�from�discourse�studies�and�corpus� linguistics�with�a�view�to�moving�towards�a�more�multi-faceted�analysis�of�corpus�data.�As�a�result,�the�dichotomies�between�the�two�fields,�as�highlighted�in�the�quotation�from�McEnery�et�al.�(2006)�at�the�beginning�of�this�chapter,�will�not�be�so�pronounced�and,�by�extension,�the�issues�raised�regarding�their�respective�weaknesses�also�less�conspicuous.
chapter�3
The two corpora Context�and�compilation
This�chapter�first�describes�the�two�corpora�on�which�the�research�is�based,�with�particular� reference� to� their� contextual� features� (e.g.� audience,� communicative�purpose),�as�these�are�significant�factors�in�shaping�the�discourse.�Various�aspects�of� the� compilation� and� preparation� of� the� corpora� for� subsequent� analysis� are�then�described.�In�this�regard,�of�particular�importance�are�the�issues�of�corpus�size�and�representativeness,�identification�of�types�and�lemmatization.
Contextual background of the Professional and Student corpus
Professional�Corpus�(PROFCORP)
The�professional�corpus�(PROFCORP)�comprises�60�professional�reports�on�en-vironmental�issues.�The�majority�of�these�reports�are�the�executive�summaries�of�Environmental�Impact�Assessment�(EIA)�reports�commissioned�from�the�early�to�mid�1990’s�by�the�Hong�Kong�Environmental�Protection�Department�(EPD)�or�Civil�Engineering�Department�from�various�environmental�consultancy�compa-nies�in�Hong�Kong.�These�are�solicited�reports,�written�in�response�to�a�‘Request�for�Proposal’,�which�document� the�potential� environmental� impacts� that� could�arise� from�the�construction�and�operation�of�proposed�buildings/facilities.�The�aim�of�the�reports�is�to�suggest�possible�mitigation�measures�which�could�be�im-plemented�to�alleviate�any�possible�adverse�environmental�effects.�It�is�to�be�noted�that�most�of�the�companies�specify�a�template�for�structuring�the�reports,�so�it�is�not�uncommon�to�find�variations�of�the�main�headings�‘Environmental�Impacts’�and�‘Mitigation�Measures’�across�many�of�the�reports.
In�some�cases,�the�reports�are�co-authored,�but�they�are�always�checked�over�and�endorsed�by�a�senior�engineer�before�being�submitted�to�the�EPD.�They�are�written� by� both� native� speakers� and� non-native� speakers� of� English,� although�in�the�Hong�Kong�context�care�is�needed�in�defining�the�concept�of�non-native�speaker.� Some� of� the� engineers� working� in� these� companies� are� referred� to� as�ABC�(American-Born�Chinese),�while�others�have�undertaken�their�tertiary�edu-
22� Corpus-based�Analyses�of�Problem-Solution�Pattern�
cation,�and�possibly�their�secondary�schooling�in�the�States,�UK�or�Canada.�As�a�result,�they�have�English�which�is�almost�indistinguishable�from�that�of�educated�speakers�of� those�afore-mentioned�countries.�However,�what� is� at� issue�here� is�not�so�much�whether�the�writers�are�native�or�non-native�speakers,�but�whether�they�are�competent�writers�of�the�type�of�written�professional�documentation�un-der�investigation.�It�should�be�noted�that�this�data�collection�of�the�EIA�reports�took�place�before�the�1997�handover�when�Hong�Kong�was�a�British�territory�and�UK-based�consultancy�companies�dominated�the�bidding�for�government�con-tracts.�The�senior�engineer-in-charge�who�vetted�the�final�version�of�the�reports�would�have�been�British�and�therefore�these�reports�can�be�considered�as�written�in�British�English.�This�background�information�has�important�implications�for�the�choice�of�a�contrastive�reference�corpus,�which�is�discussed�in�Chapter�5.
The�titles�of�these�reports�together�with�a�breakdown�of�the�number�of�words�in�each�and�the�consultancy�firms�who�produced�them�are�given�in�Appendix�3-1.�Each�report�was�given�a�filename,�e.g.�1_ERM,�which�could�be�used�to�identify�the�consulting�company�who�produced�the�report�and�to�differentiate�one�report�from�another�written�by�the�same�company.�
Student�Corpus�(STUCORP)
The�student�corpus�(STUCORP)�comprises�80�group�project�reports�written�by�2nd�and�3rd�year�undergraduate�Science�and�Engineering�students�on�a�Technical�Communication�Skills�course�in�the�Language�Centre�at�the�Hong�Kong�Univer-sity�of�Science�and�Technology�(HKUST).�For�this�group�project�the�assignment�guidelines�stipulate�that�students�are�expected�to�choose�an�area�for�investigation�where�a�problem�or�need�can�be�identified�on�the�basis�of�evidence�from�second-ary�and�primary�source�data�(survey�questionnaire,�interview,�observation),�and�propose�a�set�of�recommendations�on�the�basis�of�the�identified�problem�or�need.�No�templates�are�provided�in�order�to�discourage�students�from�over-relying�on�‘model� examples’,� although� the� in-house� produced� student� textbook� does� give�several�examples�of�reports,�which�the�students�can�draw�on�for�their�own�project�reports.�Instead�of�providing�a�template,� the�student�textbook�reviews�different�organisational�structures�(i.e.�part-by-part,�or�whole-by-whole)�and�types�of�sub-headings�(i.e.�structural,�topical)�with�the�aim�of�encouraging�students�to�choose�the�most�appropriate�one�for�their�report.
All� the� topics�of� the�student�reports� in�STUCORP�differ� from�those�of� the�professional�reports� in�PROFCORP,�as� they�relate� to� the�university�and�mostly�concern�departmental�or�service�unit�issues�which�are�of�importance�to�the�stu-dents�in�some�way.�The�titles�of�the�student�reports�together�with�a�breakdown�
� Chapter�3.� The�two�corpora� 23
of�the�number�of�words�in�each�and�the�general�topic�areas�which�they�cover�are�given�in�Appendix�3-2.�As�with�all�the�reports�in�PROFCORP,�each�report�was�assigned�a�filename,�e.g.�1_CS,�which�could�identify�the�topic�of�the�report,�with�the�first�digit�in�the�filename�used�to�distinguish�one�report�from�another�on�the�same�topic.
In�one�sense,� the�student�reports�can�be�considered�as�solicited�as�they�are�an� assessed� assignment� as� part� of� an� academic� requirement.� In� another� sense,�though,�unlike�the�EIA�reports� in�PROFCORP,�these�reports�are�unsolicited� in�that�the�students�write�the�report�on�the�basis�of�a�problem�perceived�by�them�rather�than�in�response�to�a�request�by�a�department�to�investigate�an�issue.�Be-cause�the�reports�are�unsolicited,�the�students�have�to�make�a�strong�case�for�the�existence�of�a�problem,�as�the�departments�concerned�either�might�not�be�aware�that�a�problem�exists,�not�realise�its�import,�or�may�not�agree�that�there�is�a�prob-lem.�This�is�why�most�of�the�material�in�the�student�textbook�is�devoted�to�the�aspect�of�providing�evidence�for�a�problem�through�gathering�data�from�primary�and�secondary�sources.�Moreover,�although�these�reports�are�of�a�technical�na-ture,�the�guidelines�specify�that�the�report�must�be�written�for�management,�i.e.�a�non-specialist�audience.�Appendix�3-3�presents�the�rubrics�for�the�assignments�and�some�extracts�from�the�textbook�designed�to�sensitise�students�to�key�aspects�of�the�project�reports.
Although�PROFCORP�and�STUCORP�are�the�product�of�two�different�dis-course�communities,�the�two�corpora�are,�in�fact,�similar�in�two�main�respects:�length�and�text�type.�First�of�all,�the�two�corpora�are�of�comparable�size�–�each�contains�approximately�225,000�running�words�(see�Appendices�3-1�and�3-2).�Sec-ondly,�the�fact�that�the�reports�in�PROFCORP�fall�under�the�category�of�Environ-mental�Impact�Assessment�reports�implies�that�they�are�recommendation-based�by�virtue�of�their�text�type�as�an�environmental�problem�is�identified�for�future�mitigation.�Likewise,�the�reports�in�STUCORP�can�also�be�regarded�as�belonging�to�the�Problem-Solution�text�type�because,�as�mentioned�previously,�the�assign-ment�guidelines�specify�this�discourse�structure�for�the�reports,�which�is�also�in�evidence�in�some�of�the�titles,�sometimes�with�the�focus�on�the�problem�aspect�(cf.�report�no.�23�in�Appendix�3-2�entitled�Investigation of sports facilities)�or�with�the�focus�on�the�solution�aspect�(cf.�report�no.�6�entitled�Installing computer ter-minals in UST campus).�Although�some�of�the�titles�may�only�reflect�the�solution�aspect,�the�body�of�the�reports�should�provide�evidence�for�an�existing�problem�as�this�is�stipulated�in�the�assignment�guidelines.�However,�at�this�stage,�we�can-not�state�categorically�that�these�reports�are�Problem-Solution�based�as�we�only�have�external�evidence�for�this,�i.e.�in�the�form�of�the�report�guidelines,�stipulating�the�audience�and�purpose.�As�Lee�(2001)�notes,�text�categorizations�are�generally�based�on�‘external’�criteria,�i.e.�where�and�when�the�text�was�produced,�by�and�for�
24� Corpus-based�Analyses�of�Problem-Solution�Pattern�
whom�and�its�communicative�purpose,�rather�than�‘internal’�criteria�based�on�its�linguistic�characteristics.�The�purpose�of�the�analysis�in�Chapter�4�is�to�provide�linguistic�evidence�for�classifying�the�reports�in�PROFCORP�and�STUCORP�as�belonging�to�the�Problem-Solution�pattern.�
In�sum,�PROFCORP�and�STUCORP�can�therefore�both�be�regarded�as�‘spe-cialised’�on�the�grounds�that�they�are�delimited�by�a�specific�text�type,�discourse�domain�and�have�been�compiled�with�an�a�priori�purpose� in�mind.� (see�Flow-erdew�2004a�for�a�detailed�discussion�on�the�notion�of� ‘specialised’).�This�is�an�example�of�what�Sinclair�(2001:�xi)�refers�to�as�the�early human intervention (EHI)�method�–�as�opposed�to�the�late�or�delayed human intervention (DHI) associated�with�large-scale�corpus�analysis�–�where�the�analysts�have�a�clear�goal�at�the�out-set�and�thus�construct�a�corpus�and�decide�on�the�methodology�with�a�specific�purpose�in�mind.
In�the�following�sections�various�aspects�concerning�compilation�of�the�two�corpora�are�discussed�with�reference�to� issues�raised�in�the� literature.�Method-ological� issues� regarding� identification� of� types� and� lemmatization,� which� are�related�not�only�to�the�type�of�corpus�under�investigation,�but�also�to�the�line�of�linguistic�enquiry,�are�also�addressed.�
Issues in corpus compilation
Size�and�representativeness
Several�corpus�linguists�have�raised�issues�concerning�the�size�and�representative-ness�of�specialized�corpora.�In�fact,�these�are�thorny�issues,�which�have�also�been�widely�debated�in�the�literature�on�corpus�studies�in�general,�and�to�which�there�seem�to�be�no�easy�answers.
A�commonly�held�view�is� that�the� larger�the�corpus,� the�better� it� is� for�ex-tracting� linguistic� information:� ‘Regarding� the� question� of� corpus� size,� writers�are�unanimous�in�arguing�that�in�principle�bigger�is�better�(Sinclair�1991).�The�more�text�there�is�in�a�corpus,�the�more�likely�it�is�to�give�an�accurate�representa-tion�of�the�language�and�an�adequate�number�of�examples�of�a�given�key�word’�(Flowerdew�1996:�100).�While�this�is�true�in�general�terms,�this�whole�question�of�what�is�considered�to�be�an�appropriate�size�for�a�corpus�is�highly�dependent�on�the�phenomenon�one�is�investigating.�As�other�researchers�have�pointed�out�(de�Haan�1992),�there�is�no�ideal�size�for�a�corpus�and�the�suitability�of�the�sample�depends�on�the�specific�study�that�is�undertaken�and�the�needs�and�purposes�of�the�investigation.
� Chapter�3.� The�two�corpora� 25
With� regard� to� the� investigation� of� specific� items,� McEnery� and� Wilson�(2001:�154)�point�out� that� the� lower� the� frequency�of� the� feature�one�wishes� to�investigate,�the�larger�the�corpus�should�be.�This�would�apply�to�nouns,�adjectives,�adverbs�etc,�(i.e.�content�words)�which�tend�to�have�a�much�lower�frequency�than�grammatical�words�in�any�given�corpus.�Conversely,�one�can�argue�that�smaller�corpora�can�be�used�for�investigating�the�more�common�features�of�language�such�as�grammatical�items,�and�indeed,�Biber�(1990)�has�pointed�out�that�smaller�cor-pora�are�perfectly�adequate�for�purposes�such�as�these.�The�size�of�the�corpus�is�therefore�of�paramount� importance�and�must�be�closely�matched�with�the� fea-tures�under�investigation.�However,�here�again,�size�has�to�be�balanced�against�the�level�of�delicacy�of�the�investigation,�an�issue�touched�upon�in�Kennedy�(1998),�who� remarks�on� the�danger�of�having� too�much�output� such� that� the�data�are�unwieldy�to�work�with.�
Sinclair�(2005)�makes�a�very�strong�case�for�size�not�being�such�an�issue�as�far�as�small,�specialized�corpora�are�concerned.�Evidence�for�this�point�is�based�on� a� comparison� of� frequencies� across� two� one-million-word� corpora:� LOB,� a�general�corpus,�and�the�Hong�Kong�corpus�of�the�English�of�Computing�Science,�as�shown�in�Table�3-1.�
Sinclair�comments�thus:
This�is�only�one�example,�but�it�is�good�news�for�builders�of�specialised�corpora,�in�that�not�only�are�they�likely�to�contain�fewer�words�in�all,�but�it�seems�as�if�the�characteristic�vocabulary�of�the�special�area�is�prominently�featured�in�the�fre-quency�lists,�and�therefore�that�a�much�smaller�corpus�will�be�needed�for�typical�studies�than�is�needed�for�a�general�view�of�the�language.� (Sinclair�2005:�15)
Sinclair’s�frequency-based�evidence�that�specialised�corpora,�by�their�very�nature,�do�not�exhibit�as�much�internal�variation�as�general�corpora,�is�a�factor�that�has�implications� for�not�only� the� size�of� the� corpus�but� also� its� representativeness.�The�greater�the�variation�in�the�corpus�text�under�study,�the�more�samples�and�a�larger�corpus�are�required�to�ensure�representativeness�and�thus�validity�of�the�data� (Meyer�2002).�See�also�McEnery�and�Wilson�(2001:�63–66),� for�a�detailed�
Table 3-1. Comparison�of�frequencies�in�a�general�and�a�specialised�corpus�(Sinclair�2005:�15)
LOB HK %
Number�of�different�word-forms�(types) 69990 27210 39%Number�that�occur�once�only 36796 11430 31%Number�that�occur�twice�only � 9890 � 3837 39%Twenty�times�or�more � 4750 � 3811 *0%200�times�or�more � � 471 � � 687 (69%)
26� Corpus-based�Analyses�of�Problem-Solution�Pattern�
discussion�on�corpus�representativeness.�In�respect�of�this�issue,�it�is�well�to�heed�the�words�of�Tognini-Bonelli�(2001:�57):�‘We�should�always�bear�in�mind�that�the�assumption� of� representativeness� “must� be� regarded� largely� as� an� act� of� faith”�(Leech�1991:�27),�as�at�present�we�have�no�means�of�ensuring�it,�or�even�evaluat-ing�it�objectively�(see�also�Sinclair�1991:�9).
This�vexing�issue�of�corpus�representativeness�could�be�regarded�as�more�cru-cial�as� far�as�specialized�corpora�are�concerned�on�account�of� the� fact� that� the�representativeness�of�specialized�corpora�is�usually�measured�by�reference�to�ex-ternal�selection�criteria�(i.e.�by/for�whom�the�text�is�produced,�what�is�its�subject�matter),� which� could� be� regarded� as� somewhat� subjective.� On� the� other� hand,�Williams�(2002)�sees�one�way�round�this�dilemma�by�making�a�case�for�using�in-ternal�selection�criteria�based�on�lexical�items,�which�he�argues�is�a�more�objective�means�of�ensuring�the�representativity�of�specialized�corpora.
A�complicating�factor�is�that�often�pragmatic�factors,�such�as�how�easily�the�data�can�be�obtained�come�into�play,�i.e.�the�compiler�has�to�fall�back�on�non-prob-ability� sampling� techniques� involving� “judgement”� and� “convenience”� (Meyer�2002:�44).�That�being�said,�it�is�a�sine�qua�non�that�a�specialized�corpus�should�be�of�adequate�size�such�that�there�is�a�sufficient�number�of�occurrences�of�a�linguis-tic�structure�or�pattern�to�ensure�representativeness�for�validating�a�hypothesis.�
Insofar�as�the�compilation�of�PROFCORP�and�STUCORP�are�concerned,�an�effort�has�been�made�to�ensure�that�the�corpora�are�as�representative�as�possible�for�the�type�of�writing�under�investigation.�For�example,�the�60�reports�in�PROF-CORP�were�partly�selected�on�the�basis�that�they�represent�23�different�consulting�companies,� thus� ensuring� no� one� company� style� would� dominate.� However,� it�was� impossible� to� select� an� equal� number� of� reports� from� each� of� the� compa-nies�as�access�to�these�was�dependent�on�which�ones�were�available�in�the�pub-lic�libraries.�As�a�general�rule,�though,�the�larger�the�company,�the�more�reports�were� catalogued� in� the� libraries,� so� the� distribution� of� reports� in� PROFCORP�can�be�seen�as�reflecting�the�size�of�the�company,�which�could�also�be�regarded�as�another�aspect�of�representativeness.�The�data�collection�therefore�relies�on�a�combination�of�“judgement”�and�“convenience”�sampling;�every�effort�was�made�to�collect�reports�from�as�wide�a�range�of�companies�as�possible,�but�ultimately�this�depended�on�what�was�available� in�the�public� libraries� in�Hong�Kong�(see�Meyer�2002:�42–43,�who�discusses�different�types�of�sampling�frames).�As�for�the�80�reports�in�STUCORP,�25�different�main�topic�areas�are�represented.�Some�of�the� topic�choices�are�more�popular�with�students� than�others,�but�again� this� is�represented�by�their�distribution�in�the�corpus.
As�regards�the�argument�of�using�internal�selection�criteria�as�a�more�objec-tive�means�of�ensuring�the�representativity�of�specialised�corpora,�in�Chapter�4,�I�will�demonstrate�by�internal�criteria�that�both�corpora�are�Problem-Solution�ori-
� Chapter�3.� The�two�corpora� 27
ented�and�therefore�contain�sufficient�examples�for�investigation�of�the�linguistic�structures�realising�this�particular�text�type.�Moreover,�as�each�corpus�comprises�approximately�225,000�words,�they�do�not�yield�a�quantity�of�output�that�is�over-whelming�to�work�with,�an�important�point�noted�by�Kennedy�earlier.�
Identification�of�types�
A�type�is�defined�as�each�different�word�form�whereas�a�token�is�an�individual�occurrence�of�any�word�form,�i.e.�type�(Barnbrook�1996:�53).�One�noticeable�dif-ference�between�these�reports�is�that�the�PROFCORP�consists�of�8,724�different�types�whereas�the�STUCORP�has�7,268�different�types.�This�result�runs�counter�to�what�I�expected�as�given�the�diverse�topics�of� the�student�reports�compared�with� the� focus�on�a�particular� topic� (environmental� assessment)�of� the�profes-sional�reports,�I�would�have�predicted�the�student�reports�to�contain�more�dif-ferent�types.�The�linguistic�analysis�of�key�words�in�Chapter�4�sheds�light�on�this�phenomenon.
First�of�all,� though,� it� is�of�crucial� importance�to�consider�what�constitutes�a� ‘type’� at� the� outset� as� this� will� have� a� bearing� on� the� subsequent� analysis� of�the� corpus.� In� the� following� sub-sections,� I� discuss� decisions� made� regarding�specifications�of�types,�or�word�boundaries.�As�the�reports�in�both�PROFCORP�and�STUCORP�contain�multi-word�nouns,�Latin�abbreviations�and�cases�where�sometimes�an�abbreviated�form�of�a�word�is�used�but�at�other�times�the�full�form,�it�is�necessary�to�have�a�consistent�policy�on�how�to�handle�these.
Multi-word proper nounsMulti-word� proper� nouns,� denoting� names� of� countries,� islands,� towns,� roads,�buildings,�airports�and�names�of�people,�were�treated�as�one�semantic�unit,� i.e.�one�type.�In�order�for�the�software�to�count�these�nouns�as�one�type,�the�symbol�0�was�used�in�place�of�spaces.�So,�for�example,�Hong�Kong�would�be�shown�as�Hong0Kong.�Not�surprisingly,�PROFCORP�was�found�to�contain�a�wider�range�of�examples�of�such�nouns�than�STUCORP�as� the�professional�reports�refer� to�entities�within�the�whole�of�Hong�Kong,�whereas�the�student�reports�are�mostly�related�to�university�departments�and�service�units.�
All�the�proper�nouns�were�identified�by�three�means.�First,�they�were�identi-fied�by�manually�skimming�through�the�printed�output�of�the�corpora.�Changes�were�then�made�in�the�computerised�version�of�the�corpora,�with�the�assistance�of�the�search command.�Finally,�an�alphabetical�wordlist�was�manually�checked�to�verify�that�all�instances�of�proper�nouns�had�been�collected.
28� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Latin abbreviationsIn�a�draft�wordlist,�it�was�noted�that�there�were�occurrences�of�single�letters,�which�required�further�investigation.�It�was�found�that�these�isolated�cases�of�single�let-ters�were�Latin�abbreviations�in�either�small�or�upper�case�(N.B.;�I.E.;�e.g.;�p.m.).�As�there�was�a�full�stop�separating�the�two�parts,�the�program�was�obviously�read-ing�some�of�the�Latin�abbreviations�as�two�separate�words.�The�full�stop�was�re-moved�so�all�Latin�abbreviations�would�be�read�as�one�type.���
Abbreviations vs. full formsThis�category�concerns�those�items�where�the�full�form�is�used�in�some�parts�of�the�text�(e.g.�Environmental Protection Department) but�the�abbreviated�form�in�other�parts�(e.g.�EPD) for�synonymous�pairs�of�semantically-related�items.�
Three�options�are�possible�for�dealing�with�this�situation:
(i)� treat�EPD as�one�item�and�Environmental Protection Department�as�three(ii)��treat�EPD� as� three� items� and� Environmental Protection Department� also� as�
three(iii)�treat�EPD as�one�item�and�Environmental Protection Department�also�as�one.
The�simplest�solution�would�be�to�opt�for�(i)�if�EPD�is�seen�as�functioning�as�an�anaphoric/exophoric�element,�in�fact,�similar�to�a�pronoun�which�also�either�re-fers�the�reader�back�to�a�multi-word�phrase�or�to�an�entitiy�identifiable�on�the�ba-sis�of�real�world�knowledge.�This�would�be�feasible�with�the�PROFCORP�reports�where�the�abbreviations�used,�such�as�EPD,�conform�to�the�convention�of�using�the� full� form� for� the�first�mention,�with� the�abbreviation�noted� in�parentheses�and�using�the�abbreviated�form�hereafter�in�the�text.�However,�this�standard�us-age�for�full�forms�and�abbreviations�was�not�observed�in�the�STUCORP�reports.�Students�mixed�full�forms�and�abbreviated�forms�quite�indiscriminately�in�their�reports�with�the�result�that�the�function�of�an�abbreviated�form�having�an�ana-phoric�function�was�distinctly�blurred,�as�this�was�often�taken�up�by�the�full�form.�As�it�is�imperative�to�apply�the�same�criteria�to�both�corpora�for�identifying�word�types,�option�(i)�is�therefore�rejected�on�account�of�the�arbitrary�use�of�abbrevia-tions�in�the�STUCORP.�
Another�possibility�was�option�(ii),�where�EPD�is�expanded�into�three�items.�This�option�was�also�rejected�as�although�semantically� it�would�be�a� legitimate�strategy,�it�contravenes�one’s�sense�that�EPD�functions�as�one�entity.�I�therefore�decided�to�adopt�option�(iii)�which�does�allow�EPD�to�be�considered�as�one�en-tity.�Another�reason�for�choosing�option�(iii)�is�that�it�can�also�accommodate�the�anomalous�use�of�the�full�forms�in�the�STUCORP�reports�(which�very�often�have�an�anaphoric�function�usually�taken�up�by�the�abbreviated�form)�by�treating�them�
� Chapter�3.� The�two�corpora� 29
as�one�item.�Sometimes,�it�is�not�clear�cut�as�to�how�many�items�the�full�form�con-sists�of�(cf.�Electronic�Notice Board�and�Electronic Noticeboard),�but�this�is�not�an�issue�if�option�(iii)�is�chosen.
Apart� from� EPD� used� for� Environmental Protection Department� and� EIA�for�Environmental Impact Assessment�reports,�very�few�other�abbreviations�were�found�in�the�PROFCORP.�However,�in�the�STUCORP,�four�main�categories�of�us-age�were�identified,�which,�as�can�be�seen�from�the�examples�below,�mainly�refer�to�HKUST�entities.�As�with�the�proper�nouns,�the�separate�words�in�the�full�forms�were�joined�by�using�the�symbol�0.
Departments� � Centre�of�Computing�Services�and�Telecommunications�(CCST)� � Safety�and�Environmental�Protection�Office�(SEPO)� � Student�Affairs�Office�(SAO)
Type of student according to discipline� � Computer�Science�(CS)� � Computer�Engineering�(CPEG)� � Electrical�and�Electronic�Engineering�(EEE)
� � Universities� � Hong�Kong�University�of�Science�and�Technology�(HKUST)� � Chinese�University�of�Hong�Kong�(CUHK)
� � Internet�features� � World�Wide�Web�(WWW)� � Internet�Service�Providers�(ISP)� � Electronic�Noticeboard�(ENB)
Obviously,�the�above�is�quite�a�time-consuming�process�as�all�these�adjustments�are�made�manually.�In�this�respect,�the�reader�is�referred�to�work�being�carried�out�at�the�University�of�Liverpool�(cf.�Renouf�1996�for�details�of�the�ACRONYM�project),�where�corpus�tools�are�being�developed�for�the�automatic�identification�of�thesaurally-equivalent�terms�such�as�those�listed�above.�This�project�is�referred�to�in�Chapter�5�with�respect�to�synonymous�and�hyponymous�relations�existing�between�keyword�lexis�for�the�Problem-Solution�pattern.�
In�addition�to�the�decisions�made�above�to�count�multi-word�proper�nouns,�Latin�abbreviations�and�the�full�forms�of�their�corresponding�abbreviations�as�one�type,�another�issue�which�arose�was�how�to�deal�with�the�inconsistencies�in�the�handling�of�characters�found�within�words.�Such�characters�refer�to�hyphenated�words�and�apostrophes�used�in�short�forms�of�verbs,�auxiliaries,�and�negatives,�which�are�discussed�in�detail�below.�
30� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Characters found within wordsMany�inconsistencies�in�hyphenation�occur�across�the�texts�and�sometimes�three�different�variations�of�the�same�item�were�found,�e.g.�back-up,�back up�and�also�backup.�As�the�hyphenated�words�outnumber�their�two-word�counterparts,�these�were�therefore�considered�as�the�core,�or�unmarked,�items,�so�to�speak.�For�this�reason,�all�words�which�are�hyphenated�in�the�original�text�are�treated�as�having�the�default�value�of�a�single�word.�So,�for�example,�both�back-up�and�backup�are�treated� as� the� same� type.� However,� the� Wordlist� tool� identifies� back up� as� two�separate� types.� These� typographical� variations� therefore� result� in� some� minor�anomalies,�but�it�was�felt�that�any�attempt�to�categorise�items�with�variant�spelling�such�as�back-up, back up�and�backup as�a�single�type�would�be�a�time-consuming�process�which�would�not�result�in�much�gain.
Like�the�hyphen,�the�apostrophe�is�another�character�that�can�occur�within�a�word�in�short�forms�of�verbs,�auxiliaries�and�negatives�such�as�It’s,�you’re,�can’t and�don’t.�All�contractions�were� left�as� they�were�rather� than�being�restored�to�their�full�form�for�two�reasons.�First,�it�is�useful�to�retain�the�contractions�in�the�original�as�they�reflect�stylistic�choices�of�the�author(s).�For�example,�there�were�no�instances�of�the�short�form�don’t in�PROFCORP�but�25�occurred�in�STUCORP�which�is�an�indication�of�the�different�levels�of�formality�in�the�reports.�Secondly,�it�would�be�very�time-consuming�to�make�adjustments�manually�in�the�corpora.�Bruthiaux�(1996),�who�discusses�the�issue�of�hyphenation�and�apostrophes�(pp.�33–34),�trawled�through�his�corpus�to�manually�separate�all�contractions�marked�by�an�apostrophe�(I’m becoming�I’ m,�for�example)�so�that�such�contractions�were�treated�as�two�words.�This�is�possible�with�his�relatively�small�corpus�of�advertise-ments�(16,075�words)�but�would�be�very� laborious�in� larger�corpora.�Thus,�the�size�of�a�corpus�is�another�vital�consideration�as�to�whether�certain�text�adjust-ments�are�expedient�or�not.�It�was�also�noted�that�there�were�five�misuses�of�the�apostrophe�s�in�STUCORP,�with�it’s substituted�for�possessive�its,�but�these�mis-spellings�were�also�retained�in�accordance�with�my�desire�to�tamper�with�the�text�as�little�as�possible.
I� therefore� did� not� change� any� of� the� output� produced� by� Wordsmith’s�Wordlist relating� to� characters,� i.e.� hyphens� and� apostrophes� within� words,� for�ease�of�expediency.�Neither�did�I�attempt�to�standardise�words�which�are�identical�phonologically�and�lexically,�but�not�orthographically�(i.e.�those�words�which�can�take�either�American�or�British�spelling,�e.g.�organize�vs.�organise)�as�this�would�have�meant�making�adjustments�in�the�text,�rather�than�relying�on�the�Wordlist output,�and�in�any�case�such�adjustments�would�have�made�very�little�difference�in�the�subsequent�analysis.�
One�area,� though,�where� identification�of�types� is�of�utmost� importance�in�the�type�of�lexico-grammatical�analysis�undertaken�in�this�corpus�analysis�is�that�
� Chapter�3.� The�two�corpora� 31
of�lemmatization.�In�the�following�section�I�first�define�lemmatization,�and�then�explain�my�rationale�for�not�lemmatizing�the�corpus.�
Lemmatization�
A�lemma�is�usually�considered�as�the�base�or�uninflected�form�of�a�word�(Biber�et�al.�1998:�29).�As�Sinclair�(1991:�42)�points�out�‘Traditionally,�the�‘base’,�or�un-inflected�form�is�used�even�when�that�form�is�hardly�ever�found�on�its�own,�or�hardly�ever�found�at�all’.�Here�the�word�‘Traditionally’�seems�to�be�setting�up�an�objection�to�this�definition,�which�Sinclair�provides�later�in�the�text�when�he�puts�forward�the�suggestion�that�the�most�frequently-encountered�form�could�equally�well�be�regarded�as�the�lemma.
Lemmatization�is�defined�as�follows�in�Sinclair�(1991):
Lemmatization�is�the�process�of�gathering�word-forms�and�arranging�them�into�lemmas�or�lemmata.�So�the�word-forms�give, gives, gave, given, giving, and�prob-ably�to give,�will�conventionally�be�lemmatized�into�the�lemma�give.�Any�occur-rence�of�any�of�the�six�forms�will�be�regarded�as�an�occurrence�of�the�lemma.�� (Sinclair�1991:�173)
However,� just�as�the�word�‘traditionally’� implies�that�defining�a� lemma�is�not�a�straightforward�procedure,�the�word�‘conventionally’�also�hints�that�lemmatiza-tion�may�not�be�as�simple�and�obvious�a�process�as�it�at�first�appears�either.�This�reservation�towards�lemmatization�is�echoed�in�Sinclair�(1992:�390–391)�‘…�it�is�conventional�to�think�of�meaning�as�constant�across�different�inflected�forms�of�a�word;�in�such�cases�the�inflections�could�be�conflated�together�into�lemmas�and�a�lemmatiser�used�to�do�the�job’.�Counter-examples�to�this�assumption�are�given�in�Sinclair�and�Renouf�(1988)�who�provide�corpus�evidence�to�demonstrate�that�the�morphological�pair�certain and�certainly behave�quite�independently�of�each�other�in�terms�of�meaning�and�usage�patterns.�(In�fact,�the�examples�of�certain�and�certainly�would�not�be�considered�by�some�as�even�belonging� to� the�same�lemma�as�they�are�not�of�the�same�word�class�as�one�is�adjectival�and�the�other�adverbial).��At�a�greater�degree�of�specificity�regarding�meaning,�Tognini-Bonelli�(2001:�92–98,�cited�in�Knowles�&�Don�2005)�questions�whether�facing�and�faced should�be�assigned�to�the�lemma�‘face’�as�the�former�has�a�concrete�meaning�(e.g.�facing forwards)�in�addition�to�its�metaphorical�meaning�(e.g.�facing a dilemma),�whereas� the� latter� retains�only� the�metaphorical�meaning� (e.g.� faced with a di-lemma).�Therefore,�even�the�notion�of�what�constitutes�a�lemma�is�debatable�from�a�meaning�potential�point�of�view.
32� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Another� problem� associated� with� lemmatization� is� that� different� forms� of�a� lemma�pattern�differently,� in�terms�of�collocation�and�colligation�(Barnbrook�1996:�50;�Stubbs�1996:�37).�Stubbs�(1996:�38)�cites�Sinclair’s�(1991)�example�of�the�lemma�SET,�of�which�the�verbal�form�set was�found�to�be�much�commoner�than�the�other�forms�and�commonest�in�past�tense�use,�with�SET�IN�tending�to�occur�in�end�of�sentence�position.�These�are�colligational�preferences.�Stubbs�(1996)�has�also�shown�how�different�lemmas�can�also�have�different�collocates.�For�example,�of� the� lemma�EDUCATE� the� form�education�mainly�collocates�with� terms�de-noting�institutions�(e.g.�further, higher, university)�whereas�the�form�educate col-locates�with�the�near�synonyms�enlighten, help, inform.�Sinclair�(1992)�proposes�that� examining� the� collocational� patterns� of� different� word� forms� can� be� used�as�a�basis�for�deciding�whether�they�belong�to�the�same�lemma�or�not.�Another�consideration�is�the�fact�that�different�forms�of�a�lemma�may�possibly�have�differ-ent�semantic�prosodies�(positive�or�negative�connotations�associated�with�a�word,�Louw�1993).
It�has�been�pointed�out�above� that�different� forms�of�a� lemma�have�differ-ent�frequencies�in�a�corpus,�may�have�different�meanings,�different�colligational�and�collocational�patterning,�may�occupy�different�positions�in�the�sentence,�and�may�possibly�have�different�semantic�prosodies�(see�Hoey�1997�for�an�elabora-tion�of�these�aspects).�As�one�of�the�main�aims�of�this�research�is�to�examine�the�Problem-Solution�pattern�from�a�phraseological�perspective,�it�would�therefore�be�counterproductive�to�lemmatize�the�corpus�automatically.
Conclusion
This� chapter� has� described� the� background� and� contextual� features� of� the� two�corpora,�including�a�brief�overview�of�the�literacy�practices�and�processes�(Barton�2000)� in�which�the�writing�was�constructed.�It�has�also�addressed�the� issues�of�corpus�size�and�representativeness,�with�a�justification�of�the�adequacy�of�the�two�corpora�in�this�respect.�The�chapter�has�also�detailed�the�methodological�proce-dures�undertaken�in�compiling�the�corpora�for�analysis.�From�the�above�discus-sion�on�identification�of�types�and�lemmatization�it�can�be�seen�that�I�have�largely�adhered�to�Sinclair’s�(1991:�21)�clean-text�policy:�‘The�safest�policy�is�to�keep�the�text�as� it� is,�unprocessed�and�clean�of�any�other�codes.�These�can�be�added�for�investigation’.�I�have�made�the�minimal�amount�of�text�adjustments�in�accordance�with�Sinclair’s�clean-text�policy�as�my�aim�is�not�to�obtain�statistical�information�from�large�amounts�of�text,�but�rather�to�use�statistical�tabulation�of�the�corpus�evidence�as�a�starting�point�for�qualitative�analysis�of�specific�features�in�two�spe-cialised�corpora,�using�the�Concord�and�KeyWords�tools.
chapter�4�
Frequency, key word and key-key word analysis of signals for the Problem-Solution pattern
The�first�section�of�this�chapter�describes�the�Appraisal�framework�of�Inscribed�and�Evoking�categories,�borrowing�from�systemic-functional�linguistics,�for�clas-sifying�the�signals�of� the�Problem�Solution�pattern.�The�second�part�presents�a�frequency,�keyword�and�key-key�word�analysis�of�the�signals,�classified�according�to�Inscribed�and�Evoking�items,�with�particular�attention�paid�to�their�interface�with�another�categorisation�of�lexis,�technical�and�sub-technical�vocabulary.
As�was�noted�in�the�previous�chapter,�although�all�the�texts�in�PROFCORP�are�labelled�as�‘environmental�audit’,�which�by�its�very�definition�implies�a�type�of�recommendation�report,�and�this�is�evidenced�by�many�of�the�headings�such�as�‘Recommendations’,�we�still�need�concrete�evidence�as�proof�for�this.�The�same�also�applies�to�the�reports�in�STUCORP,�many�of�which�are�also�explicitly�labelled�as�recommendation-based�by�virtue�of�their�titles�and�by�the�guidelines�given�to�students�for�this�writing�task.�Identification�of�the�signals�for�the�Problem-Solu-tion�pattern�through�computational�techniques�would�provide�internal�linguistic�evidence�for�classifying�both�the�PROFCORP�and�STUCORP�as�Problem-Solu-tion�based,�which�as�noted�in�the�previous�chapter,�is�a�more�reliable�indicator�of�representativeness�than�relying�solely�on�external�criteria.
Classificatory framework for signals: Appraisal system
Signals�for�the�Problem-Solution�pattern,�by�their�very�nature,�are�evaluative�and�thus�there�needs�to�be�a�categorization�which�accommodates�this�inherent�qual-ity.�It�was�therefore�felt�that�utilising�the�Appraisal�system�for�encoding�attitude�from� the� systemic-functional� tradition� (Martin� 2000,� 2003)� would� provide� an�ideal�framework.��
34� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Inscribed�vs.�Evoking�items
According�to�the�Appraisal�system,�items�are�classified�as�Inscribed�or�Evoking.�The�Inscribed�option�realises�lexis�which�is�explicitly�evaluative.�This�would�cor-respond�to�the�items�in�Proctor’s� list�referred�to�in�Chapter�1,�such�as�problem, fault, drawback,� where� the� evaluation� is� built� into� the� word,� as� it� were.� Carter�(1992)�also�recognises�this�evaluative�quality�inherent�in�some�of�these�Inscribed�Vocabulary�3�items�which�serve�the�same�function�as�Francis’�A-nouns�mentioned�in�Chapter�1:�‘An�interesting�category�of�A-nouns�are�those�which�generally�signal�attitudes.�Such� items�do�more� than�merely� label� the�preceding�discourse.�They�mark�it�in�an�interpersonally�sensitive�way�revealing�the�writer’s�positive�or�nega-tive�evaluation�of�the�antecedent�proposition’�(p.�80).�Here,�Carter�touches�upon�an�important�distinction�between�Inscribed�and�Evoking�lexis�regarding�the�read-er/writer�orientation�towards�the�text,�as�also�pointed�out�in�Hoey�(2001):� ‘The�writer�inscribes�the�evaluation;�on�the�other�hand,�it�is�the�word�that�evokes�(or�provokes)�an�evaluation�in�the�reader’�(p.�126).�
The� Evoking� option� ‘draws� on� ideational� meaning� to� ‘connote’� evaluation,�either� by� selecting� meanings� which� invite� a� reaction� or� deploying� imagery� to�provoke�a�stance’�(Martin�2003:�18).�In�this�model,�it�is�the�‘invite’�option�of�the�Evoking�category�I�am�interested�in,�where�the�item,�taken�out�of�context,�would�evoke� an� evaluative� response� in� the� reader.� For� example,� items� such� as� cancer and�dust in�Jordan’s�list,�which�as�Proctor�argues�are�lexical�realisations�of�the�P.�[Problem]�signal�rather�than�signals�by�and�of�themselves,�would�belong�to�this�category.�Yet�another�option�would�seem�to�exist�for�the�Evoking�category�where�it�would�only�be�possible�to�tell�from�the�context�whether�an�item�such�as�landfill�evokes�a�positive�or�negative�semantic�prosody�(see�Thompson�&�Hunston�2000�for�a�discussion�of�the�role�of�context�in�bringing�out�this�element�of�evaluation).�Although�Partington�(2001)�does�not�use�the�terms�Inscribed�and�Evoking�in�his�discussion�on�investigating�connotation,�Inscribed�lexis�seems�to�be�what�he� is�referring�to�when�he�mentions�that�where�connotation�is�so�intrinsic�to�a�word�it�is�taken�for�granted,�i.e.�writer-initiated,�and�Evoking�lexis�when�he�discusses�examples�where�the�connotation�seems�less�intrinsic,�which�can�be�based�on�situ-ational�or�cultural�factors.�
However,� the� distinctions� between� Inscribed� and� Evoking� evaluative� lexis�are�by�no�means�as�clear-cut�as� the�definitions�above�seem�to�suggest.�Most�of�the�items�considered�as�Inscribed�lexis,�where�the�word�is�intrinsically�evaluative,�would� be� superordinate� categories� such� as� problem,� solution,� but� this� category�could�also�encompass�more�specific�terms�such�as�inefficient,�unsatisfied,�where�the�evaluation�is�explicitly�signaled�by�adjectival�prefixes�such�as�in-,�un-�or�non-.�The�Evoking�category,�which�covers�items�where�the�evaluation�is�less�intrinsic,�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 35
also�raises�queries�for�classification�of�evaluative�lexis.�In�this�category,�I�would�also�include�items�such�as�pollution�for�the�reason�that,�although�such�lexis�does�have�an�intrinsic�negative�connotation,�which�would�seem�to�qualify�it�for�mem-bership�of�the�Inscribed�class,�it�is�not�a�superordinate�term,�but�rather�acting�as�a�hyponym�of�problem.�I�would�therefore�like�to�argue�that�as�lexis�such�as�problem�and�pollution�have�different�hyponymic�status,�they�should�be�accorded�a�different�classification.�Other�terms,�such�as�noise clearly�fit�into�the�Evoking�category�as�their�negative�connotation�is,�to�a�large�extent,�induced�by�the�reader’s�interpreta-tion�of� it.� It�would�seem�that� the�difficulties�with�Martin’s�classification�mainly�arise� from� the� fact� that� this� kind� of� evaluative� lexis� occurs� more� along� a� cline�and�does�not�easily�lend�itself�to�being�shoehorned�into�two�discrete�categories,�a�point�also�discussed�in�Flowerdew�(2003,�2004b).�Nevertheless,�in�spite�of�these�classification�difficulties,�it�has�much�to�recommend�it�in�highlighting�the�overall�patterning�of�different�kinds�of�evaluative�lexis�in�the�two�corpora�(see�Hunston�1993,�1994�for�detailed�descriptions�of�evaluation�in�scientific�text�and�Camiciotti�&�Tognini-Bonelli�(eds.)�2004�for�discussions�on�conceptualisation�and�recogni-tion�of�evaluation).
Frequency analysis of signals
Starting�at�a�very�general�level,�it�would�be�useful�to�compare�the�100�most�fre-quent�words�in�STUCORP�and�PROFCORP�with�the�100�most�frequent�words�in�two�general�large-scale�corpora�covering�a�wide�variety�of�genres.�The�general�corpora�chosen�were�COBUILD�listed�in�Sinclair�(1986:�192)�and�the�core�written�component�of� the�BNC�(http://info.ox.ac.uk/bnc)�as� these� two�corpora�contain�a� wide� variety� of� genres� and� are� the� most� recent� large-scale� corpora� available,�although�compiled�in�the�1990s.�The�aim�of�using�this�approach�is�to�get�a�very�general�indication�whether�both�PROFCORP�and�STUCORP�contain�high�fre-quency�signals�relating�to�the�Problem-Solution�pattern.�
Appendix�4-1�shows�the�100�most�frequent�words�occurring�across�these�four�corpora.�A�striking�difference�between�the�composition�of�the�two�general�corpo-ra�and�PROFCORP�and�STUCORP�is�that�the�two�general�corpora�mainly�consist�of�function,�i.e.�grammatical�words,�whereas�content�words�tend�to�dominate�in�the�other�two�corpora.�(In�the�following�analyses,�the�order�of�frequency�is�given�in�brackets�for�ease�of�reference�in�the�wordlists).
36� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Grammatical�signals�
An�examination�of�the�wordlists�of�the�core�written�component�of�the�BNC�sam-pler�and�COBUILD�reveals�quite�a�lot�of�similarity�between�the�two�lists�in�that�most�of�these�items�consist�of�similar�function�words,�which�are�grammatical�in�nature.�Interestingly,�in�COBUILD�but (20)�could�be�a�grammatical�signal�antici-pating�a�Problem�statement,�but�this�could�only�be�verified�with�reference�to�the�context.�Likewise,�in�the�BNC�the�modal�verb�should (75), which�is�often�used�for�proposing�solutions�and�making�recommendations,�may�well�signal�a�Response,�but�again,�we�cannot�know�this�without�recourse�to�the�context.�In�these�two�gen-eral�corpora,�these�are�the�only�two�items�in�the�100�most�frequent�words�which�hint�at�the�Problem-Solution�pattern.�
Similar� grammatical� items� are� also� found� in� PROFCORP� and� STUCORP:�however (72),�but (87)�and�should (55) in�STUCORP.�A�corpus�analysis�of�how-ever (100)� from� PROFCORP� was� provided� in� Chapter� 1� to� illustrate� the� type�of�phraseological�analysis�that�can�be�undertaken.�There�are�no�other�potential�grammatical�signals�for�the�pattern�in�either�corpus.�One�reason�for�this�could�well� be� that� these� belong� to� a� closed� set� of� items� where� few� choices� are� avail-able�in�the�grammatical�system,�whereas�vocabulary�items�constitute�a�more�open�set�from�which�a�far�greater�number�of�choices�are�available�to�express�the�same�meaning.�
It�has�already�been�noted�that�STUCORP�and�PROFCORP�contain�more�con-tent�than�function�words�in�the�100�most�frequent�words.�In�the�following�section,�I�examine�the�nature�of�these�content�words�to�determine�which�ones�signal�ele-ments�of�the�Problem-Solution�pattern,�and�consequently�which�kind�of�lexis�they�constitute,�Inscribed�or�Evoking,�on�the�one�hand,�and�technical�or�sub-technical,�on�the�other.�
Lexical�signals:�Inscribed�vs.�Evoking�
In�STUCORP,�there�are�three�Inscribed�items:�problem (58), need (86) and�result (84),�which�could�either�be�a�noun,�part�of�the�verb�phrase�result in,�or�part�of�the�sentence�connector�As a result.�In�PROFCORP�the�emphasis�is�clearly�on�the�Solution�element,�denoted�by�the�following�Inscribed�items:�measures�(36),�pro-posed�(43),�mitigation�(45),�and�recommended�(63),�with�monitoring (49)�and assessment�(62)�for�the�Evaluation�element.�As�pointed�out�in�Chapter�3�these�Vocabulary�3�items�could�function�textually,�i.e.�as�connectives,�and�also�as�lexis�which�operates�at�a�more�local�level�of�signalling.�However,�clearly,�frequency�lists�cannot�provide�this�kind�of�detailed�information.
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 37
Now�I�will�discuss�examples�of�the�other�type�of� lexical�signal�–�Evoking�–�where�the�item�evokes�some�kind�of�evaluation�when�considered�out�of�context�in�relation�to�the�reader’s�conventional�interpretation�of�it.�These�Evoking�items�differ�from�the�Inscribed�items�described�in�the�previous�paragraph�as�they�are�the�lexical�realisations�of�the�Problem�statement�whereas�the�Inscribed�items�are�the�actual�signals�for�the�Problem.�Items�in�PROFCORP�which�would�appear�to�fit�this�category�include�the�following:�noise (14),�traffic�(51),�waste�(53)�and�dust�(72).�Impacts�(22)�and�impact�(30)�are�also�included�here�because�in�the�context�of�environmental�studies�they�imply�a�negative�effect.�In�the�Dictionary of Ecology and Environment (1995)�impact assessment is�defined�as�‘evaluation�of�the�effect�upon�the�environment�of�a�large�construction�programme’�(p.�122).�Disposal (98)�is�an�Evoking�item�for�the�Solution�element.�As�mentioned�in�the�previous�chapter�there�also�exists�another�kind�of�Evoking�item�where�we�can�only�tell�from�the�context�whether�the�word�has�a�positive�or�negative�semantic�prosody.�In�PROF-CORP,�construction (16),�landfill�(71)�and�reclamation�(84)�are�examples�of�this�type�of�items�as�they�can�be�regarded�as�either�an�element�of�the�Problem�or�Solu-tion,�i.e.�they�can�cause�a�problem�or�be�put�forward�as�a�solution.�For�example,�out�of�the�356�tokens�of�landfill,�which�occurs�as�a�key�word�in�ten�reports�(see�Table�4-2),�125�of�these�indicate�some�kind�of�problem,�e.g.�…quantities of gas are being generated within the landfill.�It�is�of�interest�to�note�that�this�type�of�Evoking�lexis�does�not�occur�in�the�STUCORP�word�frequency�list.�
The�signals,�which�are�overwhelmingly�lexical�in�nature,�are�therefore�clearly�Problem-Solution� based,� with� Inscribed� items� occurring� in� both� PROFCORP�and�STUCORP,�but�Evoking�items�only�found�in�PROFCORP.�A�final�observa-tion�is�that�in�PROFCORP�the�Inscribed�items�relate�to�the�Solution�whereas�the�Evoking�items�tend�to�focus�on�the�Problem�aspect�both�in�terms�of�a�wider�range�and�higher�frequency�of�occurrence.�
Lexical�signals:�Technical�vs.�sub-technical�
Apart� from� this� evaluative� dimension� discussed� above� from� the� perspective� of�Inscribed� vs.� Evoking� items,� we� can� also� ask� questions� about� the� subject� mat-ter�of�the�lexis.�An�examination�of�the�data�points�to�an�interesting�intersection�of�Inscribed�and�Evoking�lexis�with�sub-technical�vocabulary.�Following�Baker’s�(1988:�92)�definition�of�sub-technical�as�‘items�which�express�notions�general�to�all�or�several�specialised�disciplines,�e.g.�factor,�method�and�function’,�as�a�general�rule,� the� Inscribed� lexis,� e.g.� problem, need, recommended,� tends� to� be� sub-technical�vocabulary�as�such�items�have�a�discourse�role�and�are�therefore�also�categorised�as�Vocabulary�3�items.�
38� Corpus-based�Analyses�of�Problem-Solution�Pattern�
On�the�other�hand,�the�Evoking�lexis�highlighted�by�the�frequency�analysis�of�PROFCORP�would�seem,�at�first�sight,�to�constitute�another�type�of�sub-technical�vocabulary.�This�is�because�words�such�as�noise, construction, traffic, waste, dust etc.,�whilst�having�a�high�frequency�of�occurrence�within�the�area�of�environmen-tal�studies,�are�also�used�in�general�English.�However,�I�say�at�first�sight,�because�in�the�Dictionary of Ecology and Environment�(1995),�many�of�these�words�are�listed�as�collocating�with�other�nouns�and�adjectives,�thus�expressing�a�concept�particu-lar�to�the�field�of�environmental�studies.�For�instance,�ambient noise is�defined�as�‘=�general�noise�which�surrounds�an�organism�(such�as� traffic�noise,�waterfalls�etc.)’�(p.�10).�When�a�noun�in�common�usage�is�found�to�have�a�strong�colloca-tion�with�a�particular�noun�or�adjective�in�this�field,�I�would�argue�that�it�takes�on�a�technical�meaning�and�no�longer�meets�one�of�the�definitions�of�sub-technical.�In�other�words,�ambient noise�is�therefore�a�technical�term�as�it�does�not�occur�in�general�English�usage�and�for�this�reason�is�to�be�considered�as�a�multi-word�unit�rather�than�a�collocation.�Another�common�(technical)�collocation�in�this�field�is�sensitive receiver which�is�actually�defined�in�one�of�the�reports�as�‘A�sensitive�receiver�is�a�receiver�considered�sensitive�to�given�impacts�from�changes�in�noise�or�air�quality,�vibration,�land�use,�visual�or�landscape�impacts’.
The�above�examples�illustrate�that�lexis�can�change�from�being�sub-technical�to�technical�in�nature�by�virtue�of�its�collocational�behaviour�(see�Williams�1998;�Yang�1986,�for�corpus-based�research�on�technical�and�sub-technical�vocabulary�in�the�fields�of�biology�and�engineering,�respectively).�It�is�therefore�necessary�to�expand�the�traditional�classification�frameworks�for�technical�and�sub-technical�vocabulary�based�on�single�word�items�to�also�include�multi-word�combinations.�
I�will�now�re-examine�some�of�the�Evoking�items�from�the�frequency�list�in�light�of�the�above�argument�with�reference�to�the�definitions�provided�by�the�Dic-tionary of Ecology and Environment.�The�definition�for�disposal when�it�collocates�with�land�and�marine is�given�as�‘depositing�waste�in�a�hole�in�the�ground’�and�‘depositing�waste�at� sea’.�When�disposal collocates�with�refuse,�waste or�sewage,�it�has�the�meaning�of� ‘getting�rid�of�something’.�(p.�71).�In�addition�to�ambient noise�(general�noise�surrounding�an�organism),�I�would�argue�that�all�these�items�of�Evoking�lexis�are�technical�as�they�would�not�commonly�be�found�in�general�English.�Using�the�Concord tool�in�Wordsmith would�verify�whether,�for�example,�noise,�if�it�collocates�with�ambient�should�be�regarded�as�technical�or�sub-techni-cal.�This�point�will�be�taken�up�in�Chapter�6.
The�same�case�can�be�made�for�Inscribed�items,�e.g.�Impact Assessment (eval-uation�of�the�effect�upon�the�environment�of�a�large�construction�programme)�for�the�Problem�element�and�Mitigation Measures (measures�taken�to�offset�these�negative�effects)�for�the�Solution�element.�In�fact,�both�these�collocations�are�of-
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 39
ten�used�as�sub-headings�in�the�environmental�audit�reports,�which�denotes�the�discourse-organising�status�of�this�Inscribed�lexis.
The� foregoing�discussion�has� shown�that� in�STUCORP�there�are�a� few�In-scribed�items��such�as�problem�and�need�which�could�be�categorised�as�sub-tech-nical�vocabulary.�On�the�other�hand,�PROFCORP�contains�a�higher�proportion�of�Evoking�items�such�as�noise,�construction,�and�traffic,�which,�taken�as�they�stand,�are�also�sub-technical�but�could�also�be�classified�as�technical�by�virtue�of�their�collocational�behaviour.�However,�this�point�still�needs�to�be�verified.�This�analysis�may� therefore� challenge� the�assumption� that� lexical� signals� are�always�sub-technical� vocabulary,� but� more� data� on� collocational� patterning� is� needed�before�this�can�be�accepted.��
Notwithstanding,� preliminary� examination� of� these� frequency� lists� has�thrown�up�some�interesting�data�for�more�in-depth�analysis�in�subsequent�chap-ters.�These�preliminary�data�do� suggest� that�both�PROFCORP�and�STUCORP�differ�from�the�two�general�corpora�in�their�fundamental�makeup�by�virtue�of�the�high�frequency�of�several�lexical�items�of�the�Problem-Solution�pattern,�although�it�has�been�noted�that�these�are�far�more�prevalent�in�PROFCORP,�especially�of�the�Evoking�type.�I�have�also�proposed�a�redefinition�of�the�notion�of�technical�vs.�sub-technical�vocabulary�to�take�into�account�collocational�patterning.�This�point�will�be�revisited�and�explored�in�greater�detail�in�Chapters�6�and�7.
However,�such�type�of�frequency�data�only�gives�us�a�very�general�overview.�A�key�word�analysis,�by�revealing�words�of�unusually�high�frequency�as�outlined�briefly�in�Chapter�1,�would�provide�more�insights�into�the�genre�or�discourse�pat-terns�of�the�corpus.��
Key word analysis of signals
Scott’s� (1997)� starting�point� is�with� the�concept�of� “aboutness”� (Phillips�1989);�i.e.� the� content� of� the� text,� which� relates� to� Halliday’s� (1994)� ideational� meta-function.�Moreover,�like�Hoey�(2001)�he�also�recognises�that�a�text’s�“aboutness”�depends�on�the�reader’s�decoding�of�the�text.�‘Aboutness�is�a�function�of�a�text-in-the-world:�that�is,�it�needs�a�human�reader�or�listener�to�perceive�it,�to�decide�what�it�is.�The�text�alone�is�only�potentially�“about”�something’�(Scott�2000a:�107).�He�then�goes�on�to�ask�where�is�this�“aboutness”�located�in�the�text�and�how�is�it�signalled�internally.�He�answers�this�by�offering�the�concept�of�“keyness”,�‘a�word�which� occurs� with� unusual� frequency� in� a� given� text’� (In� fact,� a� key� word� can�be�either�positive,�i.e.�of�unusually�high�frequency,�or�negative,�of�unusually�low�frequency).�The�computational�procedure�for�identifying�a�key�word�is�a�purely�mechanical�procedure�which�does�not�rely�on�a�knowledge�of�English�or�world-
40� Corpus-based�Analyses�of�Problem-Solution�Pattern�
knowledge:�the�identification�of�key�words�is�through�a�comparison�of�the�repeti-tion�of�word-types�with�word-tokens�(Scott�1996–1999).�
This�key�word�procedure�has�been�used�for�uncovering�stylistic�features�(Scott�&�Tribble�2006),�identifying�the�genre�to�which�a�text�belongs�(Tribble�2002)�and�revealing�sub-technical�and�non-technical�vocabulary�in�engineering�texts�(Mu-draya�2005).�However,�to�my�knowledge,�apart�from�a�small-scale�study�by�Scott�(2001b),�no�large-scale�computational�analysis�of�signals�for�elements�of�the�Prob-lem-Solution�pattern�has�been�undertaken�to�date.�
I� will� now� examine� the� key� words� in� each� corpus� as� a� whole� and� then� in�each�separate�report�to�determine�whether�these�display�lexical�signals�or�lexical�realisations�for�the�pattern.�My�hypothesis�would�be�that�the�key�words�in�each�text�file,�i.e.�report,�would�contain�signals�for�elements�of�the�pattern,�thereby�pro-viding�further�evidence�for�classifying�these�reports�as�Problem-Solution�based�because�the�pattern�itself�was�so�salient�and�the�link�between�particular�words�and�roles�the�pattern�so�fixed.�For�the�reference�corpus,�I�used�the�one-million�word�core�written�component�of�the�BNC,�as�I�did�not�have�access�to�the�full�BNC�at�the�time.�However,�this�general�corpus�is�still�satisfactory�for�my�purposes�as�it�con-tains�86�texts�from�nine�different�subject�domains.�But�it�is�well�to�bear�in�mind�that�‘Times�Change,�and�so�do�Corpora’�(to�borrow�a�title�from�Johansson�1991).�With� the�growing� importance�attached� to� the� role�of� ‘International�English’�or�ELF,�English�as�a�Lingua�Franca,�in�corpus-based�studies�(see�Mauranen�2003;�Se-idlhofer�2001),�it�is�expected�that�future�research�will�draw�on�the�localized�data�of�the�International�Corpus�of�English,�such�as�ICE–HK,�for�exploitation�as�suitable�contrast� corpora� (see� http://www.hku.hk/english/research/icehk/index.htm).� (I�do�not�regard�PROFCORP�as�having�the�full�status�of�international�English�be-cause�of�the�socio-cultural�conditions�in�which�the�reports�were�constructed�(see�Chapter�3)�and�for�this�reason�PROFCORP�may�have�more�in�common�with�the�variety�of�English�in�the�ICE-GB�component).�
In�addition�to�the�choice�and�size�of�reference�corpus,�another�consideration�when�using�the�key�words�procedure�is�the�cut-off�point�for�the�Log�Likelihood�which�is�set�by�establishing�a�minimum�significance,�i.e.�p�value;�the�smaller�the�p�value�the�fewer�key�words�in�the�display.�For�computing�the�keyness,�the�p�value�was�set�at�0.000000000001�to�obtain�a�reasonable�number�of�key�words�for�analy-sis�and�the�minimum�frequency�requirement�was�left�at�the�default�value�of�3�to�avoid�being�swamped�by�data.�The�number�of�key�words�in�each�report�ranged�from�15�to�around�90�(in�the�Help�files,�Scott,�in�fact,�suggests�having�around�40�key�words�as�a�reasonable�number�on�which�to�draw�conclusions�about�a�text).�
The�following�section�summarises�the�main�findings�according�to�the�catego-ries�of�Inscribed�vs.�Evoking�lexis�with�reference�to�its�status�as�technical�vs.�sub-technical�vocabulary.�These�key�word�results�will� also�be�compared�with� those�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 41
of�the�frequency�analysis�in�the�previous�section.�(As�grammatical�items�did�not�show�up�in�the�key�word�analysis,�as�mentioned�in�Chapter�1,�they�are�not�dealt�with�here).
Lexical�signals:�Inscribed�vs.�Evoking�
The�list�of�key�words�in�each�corpus�and�in�each�individual�report�was�also�ex-amined�for� instances�of�Inscribed�and�Evoking� items�for� the�Problem-Solution�pattern.�In�PROFCORP�a�distinct�pattern�began�to�emerge�across�the�corpus�as�a�whole�and�all�the�reports.�In�the�whole�PROFCORP�corpus�15�out�of�the�top�40�key�words�can�be�classified�as�belonging�to�one�of�the�categories�for�the�pattern.�The�Evoking�items,�noise,�impacts,�impact,�waste,�traffic,�dust,�realise�the�Prob-lem�element�with�construction,�landfill�and�reclamation�having�the�potential�to�be�either�the�Problem�or�Solution�element�depending�on�the�context.�In�contrast,�the�Solution�element�is�signalled�by�key�words�of�an�Inscribed�nature,�e.g.�miti-gation,�measures,�proposed,�recommended,�with�monitoring�and�assessment�used�for�the�Evaluation�element.�These�findings�are�very�much�in�keeping�with�the�findings�from�the�frequency�analysis�and�also�mirror�the�same�kind�of�patterning�as�that�found�in�the�individual�reports.�Another�important�observation�is�that�in�every�single�report�in�PROFCORP�there�were�two�or�more�Inscribed�signals�and�four�or�more�Evoking�items�which�clearly�shows�that�not�only�the�corpus�taken�as� a� whole� but� also� each� report� can� be� considered� as� Problem-Solution� based.�Appendix�4-2�presents�the�key�words�from�a�PROFCORP�report�to�illustrate�this�finding.
In�contrast,�out�of�the�top�40�key�words�in�STUCORP�there�is�only�one�In-scribed�signal,�i.e.�problem.�Furthermore,�22�out�of�the�80�individual�reports�in�STUCORP�do�not�contain�any�key�words�which�are�obvious�as�either�Inscribed�or�Evoking�items�for�the�Problem�or�Solution�elements.�However,�in�the�remaining�58�reports�there�is�a�much�wider�range�of�Inscribed�signals�for�both�the�Problem�and�Solution�element�than�was�noted�in�the�frequency�analysis,�which�can�partly�be�explained�by�the�default�minimum�of�three.�For�example,�we�find�the�following�superordinate�words,�including�the�signal�problem,�which�all� imply�some�kind�of�problem�statement:�concern, need, failures, burden, difficulties, dissatisfac-tion, dishonesty, destruction, shortcoming, issue, demand. It�is�interesting�to�note�that�some�Inscribed�items�are�also�adjectival�in�nature,�e.g. insufficient, in-adequate, inefficient, unfair, uninteresting, unsatisfied,�where�the�negative�im-port�of�the�word�is�signalled�by�the�prefix�-in�or�-un.�These�findings�are�in�contrast�to�both�the�keyword�analysis�of�the�whole�corpus�and�also�the�frequency�analysis,�where�the�only�Inscribed�items�noted�for�the�Problem�element�were�problem�and�
42� Corpus-based�Analyses�of�Problem-Solution�Pattern�
need.�It�is�unlikely�that�the�above�key�word�items�would�show�up�in�the�100�most�frequent�words�or�as�key�in�the�corpus�as�a�whole�as�they�only�occur�as�key�in�one,�two�or�three�out�of�the�60�reports.�We�also�have�a�similar�situation�regarding�the�Inscribed�items�for�the�Solution�element,�with�solution, solutions, proposal, proposed, suggestions,� suggested, recommendations, recommended,� occur-ring�as�key� in�only�a�handful�of� the�reports.�Evoking�key�words,�e.g.�cheating, rubbish, accidents, crimes, theft, infringement, debts, penalty, overcrowding, stress,�were�also�found�in�the�individual�reports,�but�did�not�show�up�either�in�the�frequency�analysis�or�in�the�key�word�analysis�of�the�whole�corpus.�
Lexical�signals:�Technical�vs.�sub-technical�
The�key�word�list�of�each�report�in�PROFCORP�was�trawled�through�manually�to� isolate� instances�of� technical�vocabulary�occurring�as�key.� It�was� found� that�there� were� only� seven� reports� which� did� not� contain� any� technical� vocabulary�amongst�their�key�words.�A�most�interesting�finding�was�that�50�out�of�the�60�re-ports�contained�abbreviations�which�could�be�regarded�as�standing�for�‘technical�phrases’,�or�multi-word�combinations�to�use�Yang’s�(1986)�terminology.�Appendix�4-4�presents�a�list�of�the�technical�vocabulary�occurring�either�as�individual�key�words,�or�key�phrases�signalled�by�the�use�of�abbreviations.
As�can�be�seen�from�this�list,�the�majority�of�technical�vocabulary�appears�in�abbreviation�form,�which�I�have�attempted�to�classify�under�subject-related�head-ings.�There�is�one�term�under�section�H�which�deserves�special�mention�here.�In�several�reports�there�are�allusions�to�Fung Shui�(i.e.�geomancy)�matters,�which�are�“of�particular�concern�to�villagers”�and�“are�sensitive�issues”�as�they�play�a�part�in�the�decision-making�of�where�construction�should�be�carried�out.�While�Fung Shui�is�not�strictly�a�‘technical’�term,�I�have�included�it�in�this�list�as�it�demon-strates�how�cultural�issues�can�affect�technical�operations,�as�exemplified�by�the�following�extract:
Proposed offshore berths are located to the northeast of Sha Chau, therefore avoiding direct intrusion to the “Fung Shui” of the temple which has a northwest aspect.
The�remainder�of�the�technical�lexis�is�in�the�form�of�multi-word�combinations�transcribed�as�abbreviations�after�the�initial�mention�of�the�item.�As�I�have�sug-gested�earlier,�many�of�the�individual�words�in�these�abbreviations�could�be�viewed�as�sub-technical,�e.g.�facility (see�Appendix�4-1),�as�they�have�a�high�frequency�of�occurrence�in�this�field�but�are�also�used�in�general�language.�However,�when�these�words�combine�with�other�words,�the�whole�expression�takes�on�a�technical�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 43
aspect�as�it�would�not�be�found�in�general�English,�e.g.�Refuse Transfer Facili-ties, RTF�(see�section�F�in�Appendix�4-4).�Moreover,�the�use�of�abbreviations�for�these�multi-word�combinations�strengthens�the�case�that�they�should�be�regarded�as�fixed�expressions,�i.e.�as�one�conceptual�entity�having�a�technical�meaning�in�this�particular�field.�
I� will� now� examine� whether� these� key� technical� multi-word� combinations�bear�any�relation�to�the�Inscribed�and�Evoking�key�words�for�the�Problem-Solu-tion�pattern�noted�in�the�previous�section.�The�Inscribed�signal�assessment com-bines�with� impact� to� form�Impact Assessment,�which� is�a�common�key�word�term�in�section�A�‘Environmental�Study’.�As�for�the�Evoking�items,�it�was�noted�earlier�that�key�lexis�such�as�noise,�traffic,�discharge�and�waste�carries�a�nega-tive� connotation� which� would� therefore� signal� the� Problem� element.� However,�when�we�examine�the�list� in�Appendix�4-4�we�find�that�noise�and�waste occur�in�abbreviated�phrases�which�signal�the�Solution�element�–�see�for�example,�Al-lowable Noise Levels (ANL)�and�Noise Control Ordinance (NCO)�in�section�B�‘Environmental�rules�and�regulations’�and�Low-level Radioactive Waste�Storage Facility�(LRWF)�in�section�F�‘Mitigation�Measures’.�Such�abbreviations�could�be�regarded�as�a�condensed�form�encapsulating�both�problem�and�solution�elements;�for�example,�‘noise�control�ordinance’�can�be�paraphrased�as�an�ordinance�in�or-der�to�control�noise,�using�a�two-way�signaling�verb�(see�Chapter�1).�Moreover,��prosodic�meaning�may�be,�but�is�not�necessarily�accessible�via�conscious�reflec-tion.�In�cases�where�native-speaker�intuition�proves�unreliable,�corpus�data�can�be�of�use�in�uncovering�this�prosodic�meaning,�as�in�the�case�of�landfill.�An�ex-amination�of�the�corpus�co-text�reveals�landfill to�have�a�negative�meaning�when�it�combines�with�gas, i.e.�Landfill Gas (LFG)�in�section�E�‘Gases�/�Metals�causing�environmental�damage’,�but�a�positive�meaning� in�Pillar Point Valley Landfill (PPVL) in�section�F�‘Mitigation�Measures’.
An� examination� of� the� list� of� key� technical� vocabulary� in� STUCORP� (see�Appendix�4-5)�shows�that�several�of�these�items�are�computer-related,�although�some�would�argue�that�lexis�such�as�Internet,�e-mail�and�PC,�like�kg.�and�km.�in�section�G�‘Measurements’�in�Appendix�4-4,�are�sub-technical�rather�than�purely�technical�as�they�are�found�in�general�English.�However,�I�view�them�as�techni-cal�as�they�retain�the�same�meaning�in�both�general�and�more�specialised�usage,�which�conforms�with�Goodman�and�Payne’s�(1981)�definition�of�technical�terms�having�congruity�among�scientists�(unlike�the�term�‘cell’,�for�example,�which�has�a�different�meaning�in�biology�to�that�in�general�English).�Here,�we�have�an�ex-ample�of�de-terminologization�which�refers�to�a�process�whereby�specialist�terms�such�as�those�relating�to�computers�make�their�way�into�general�language�through�the�mass�media�or�direct�impact�(Bowker�&�Pearson�2002).�I�have�also�included�university�departments�and�service�centres�in�the�list�in�Appendix�4-5�for�the�rea-
44� Corpus-based�Analyses�of�Problem-Solution�Pattern�
son�that�their�abbreviated�forms,�which�occur�as�key�words,�would�not�be�found�in�general�language.�It�is�evident�that�this�list�is�quite�short,�most�probably�because�in�the�guidelines�for�this�assignment�students�are�instructed�to�explain�or�couch�technical� information�in�language�that�is�accessible�to�a�manager�who�may�not�be�a�specialist�in�the�field�(see�Chapter�2).�Unlike�in�PROFCORP,�none�of�these�technical�words�relate� to� the�Problem-Solution�pattern�and� there� is�no�overlap�between�these�technical�key�words�and�the�Inscribed�and�Evoking�items�discussed�previously.�
If�we�now�return�to�Appendices�4-2�and�4-3,�which�present�the�key�word�lists�for�one�PROFCORP�and�one�STUCORP�report,�it�is�evident�that�in�general�these�lists�consist�of�a�mixture�of�technical�and�sub-technical�vocabulary.�The�sub-tech-nical vocabulary�comprises�discourse-organising�words,� such�as� measures and�recommended�in�Appendix�4-2,�and�problems and�need�in�4-3,�and�the�techni-cal�vocabulary�largely�by�abbreviated�terms�in�the�PROFCORP�reports�(and�pos-sibly�by�collocations�although�this�has�yet� to�be�proved),�and�computer-related�terms,�e.g.�server,�dial-in�in�the�STUCORP�reports.�The�prevalence�of�abbrevia-tions�showing�up�as�key�words�of�a�technical�nature,�especially�in�PROFCORP,�suggests�that�the�use�of�abbreviations�in�scientific�writing�is�an�area�that�merits�attention�for�future�research.
Having�considered�these�vocabulary�issues,�i.e.�the�intersection�of�Inscribed�and�Evoking�items�with�technical�and�sub-technical�vocabulary,�in�relation�to�the�corpus�as�a�whole�and�to�individual�texts,�I�will�now�examine�their�keyness�across�a�number�of�texts.
Key-key word analysis of signals
Whereas�the�key�word�analysis�can�tell�us�which�words�are�key�in�a�given�text,�the�key-key�word�analysis�goes�one�step�further�by�showing�the�words�which�are�key�in�a�large�number�of�texts.�This�can�be�done�by�creating�a�database�from�the�key�words�files,�which�will�list�the�key�words�which�are�most�frequent�over�a�number�of�files.�The�key�words�technique�is�very�useful�in�revealing�the�genre�or�discourse�characteristics�of�the�corpus�as�a�whole,�through�a�set�of�semantically-related�key�words�as�mentioned�previously,�but�the�key-key�word�analysis�gives�a�more�deli-cate�analysis�by�showing�the�number�of�texts�in�which�the�word�is�found�to�be�key,�i.e.�a�word’s�“keyness”.
For�example,�each�of�the�60�reports�and�80�reports�in�PROFCORP�and�STU-CORP�respectively�will�have�its�own�key�words.�These�key�words�will�probably�fall�into�two�main�categories.�There�will�be�key�words�which�are�key�in�one�report,�but�not�generally�key�in�others.�In�the�case�of�PROFCORP,�where�the�reports�come�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 45
from�various�companies�working�on�different�projects�for�different�areas�in�Hong�Kong,�we�would�expect�the�names�of�the�companies�(e.g.�Maunsell)�and�areas�(e.g.�Wan Chai)�to�be�key�in�a�restricted�number�of�reports.�The�other�category�of�key�words�would�consist�of�more�general�lexis�which�would�be�typical�of�the�discourse�under� investigation,�and�thus�be�classed�as�“key-key”�words.� It� is�hypothesised�that� words� relating� to� the� Problem-Solution� pattern� would� occur� as� “key� key”�lexis�in�STUCORP�and�PROFCORP.�
Appendices�4-6�and�4-7�show�a�list�of�key-key�words�occurring�in�4�or�more�texts�in�PROFCORP�and�STUCORP�respectively.�Four�texts�were�chosen�as�the�cut-off� point� as� those� key� words� occurring� in� three� texts� or� below� commonly�related� to� technical� vocabulary,� e.g.� dot-matrix� in� STUCORP� or� the� names� of�companies,�e.g.�Maunsell�in�PROFCORP,�which�did�not�relate�to�elements�of�the�Problem-Solution�pattern.�
Lexical�signals:�Inscribed�vs.�Evoking�
Table�4-1�(based�on�the�statistically�prominent�words�extracted�from�Appendices�4-6�and�4-7)�shows�the�Inscribed�signals�from�each�corpus�with�the�number�of�texts�in�which�they�are�key�noted�in�brackets.�However,�as�mentioned�previously,�at�this�stage�of�the�analysis,�we�do�not�yet�know�whether�these�Inscribed�signals�
Table 4-1. Inscribed�signals�in�STUCORP�and�PROFCORP
Element STUCORP PROFCORP
Problem Problem�(8)Problems�(4)Need�(4)Insufficient�(5)
Solution Recommendations�(5) Mitigation�(43)�measures�(30)Solutions�(4) Proposed�(28)Solution�(4) Recommended�(27)
Recommendations�(8)Treatment�(9)Options�(5);�Plan�(5);�Scheme�(5)Minimise�(5)�reduce�(4)�ensure�(4)
Evaluation Feasibility�(8) Monitoring�(29)Feasible�(6) Assessment�(23)
Audit�(5)
Note.�Figures�in�parentheses�denote�the�number�of�texts�in�which�the�words�were�found�to�be�key.
46� Corpus-based�Analyses�of�Problem-Solution�Pattern�
are�functioning�at�a�textual�or�local�level�of�coherence.�(This�aspect�will�be�covered�in�Chapters�6�and�7.)
While�the�above�table�gives�clear�proof�that�both�corpora�comprise�Problem-Solution�based�reports,�their�overall�profiles�of�the�patterning�are�somewhat�dif-ferent.�These�differences�will�be�discussed�in�detail�in�the�following�main�section.
In�PROFCORP,�Concord,�another� tool� in�WordSmith,�was�used�to�examine�certain�words�at�sentence�level,�namely�options,�plan�and�scheme,�to�determine�whether� they�qualified�as� Inscribed�signals,�and� if� so,� for�what�elements�of� the�Problem-Solution�pattern.�One�point�uncovered�by� this� analysis�was� the� func-tion� in�PROFCORP�of� seemingly�synonymous� lexis.� (This� issue�of�under�what�circumstances�one�word�is�used�in�preference�to�another�with�the�same�semantic�equivalence�was�raised�in�Chapter�1.)
For�instance,�design, project, scheme�and�plan�are�all�listed�as�synonyms�in�the�Collins Bank of English Thesaurus (1998),�but�in�the�context�of�the�environ-mental�reports�in�PROFCORP,�one�cannot�necessarily�be�substituted�for�another.�For�example,�design and�project are�always�used�to�designate�some�type�of�con-struction�in�the�Situation�element�and�they�occur�as�key�words�across�twelve�and�eight�reports,�respectively.�Even�though�both�words�are�used�for�the�Situation,�the�following�examples�show�that�they�are�not�interchangeable:
At present, the programme for design of the sewerage along Castle Peak Road is not determined and the pipe sizes and invert levels have yet to be decided.
Certain amendments to the construction specification have also been found necessary and have been accepted by tenderers for the project.
In�contrast,�plan� is�never�used�to�mark�the�Situation,�but�always�occurs�as�key�lexis�for�the�Solution�element,�signalling�a�solution�for�some�kind�of�environmen-tal�effect:
Contractually, a noise limit together with a noise monitoring and action plan can be specified in the contract to control noise. ��
Like�plan,�the�items,�scheme and�proposed�also�signal�a�proposed�solution,�as�in�the�two�examples�below:�
Other benefits of the Stage 1 Scheme include: the removal of pollution loads from water bodies in the Eastern and New Territories.
Fifteen measures were proposed to avoid or mitigate air quality impacts
The�above�examples�thus�highlight�the�dangers�of�relying�solely�on�a�thesaurus�for�clarification�of�meaning�as�contextual�issues�can�also�come�into�play.
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 47
It�was�not�surprising�to�find�feasibility and�feasible in�STUCORP,�and�moni-toring,�assessment and�audit�in�PROFCORP�acting�as�Inscribed�key-key�word�signals�for�the�Evaluation�element�given�the�context�of�writing.�In�STUCORP�the�Inscribed�lexis�is�used�for�assessing�the�practical�implementation�of�the�Solution�before�it�has�been�introduced,�(i.e.�is�it�feasible�to�implement�the�proposed�solu-tion�from�a�technical,�economic,�environmental�and�social�point�of�view),�where-as�in�PROFCORP�the�recommended�monitoring,�assessment�and�audit�measures�are�applied�to�the�Solution�when�it�is�already�in�place.�
The�list�of�Evoking�items�in�Table�4-2�is�valuable�in�that,�like�the�list�for�the�Inscribed�signals,�it�gives�us�a�general�profile�of�the�subject�matter�and�the�textual�patterning�of�each�corpus.�There�exist�quite�striking�differences�between�the�two�corpora,�which�will�also�be�taken�up�in�the�following�main�section.
In�PROFCORP,�there�were�problematic�areas�in�the�classification�where�it�was�necessary�to�use�the�Concord�software�in�order�to�determine�how�certain�Evok-ing�items�should�be�categorised.�Construction and�landfill are�classified�under�both�the�Problem�and�Solution�as�Concord shows�that�they�are�used�in�different�
Table 4-2. Evoking�items�in�STUCORP�and�PROFCORP
Element STUCORP PROFCORP
Problem stolen�(4) Impacts�(50)��impact�(26)Contaminated�(14)�contamination�(4)Noise�(44)�traffic�(23)�Sewage�(12)�sewerage�(6)Waste�(20)�wastes�(5)�dust�(20)Pollution�(10)�emissions�(10)�Sediments�(10)�odour�(9)Effluent�(6)�discharge�(5)�Discharges�(5)NSRS (9)Dba (8)TSP (7)leachate (6)stormwater (5)groundwater (4)*�landfill�(10)*�construction�(47)
Solution Disposal�(14)�implementation�(6)Barriers�(5)Ordinance�(4)*�Landfill�(10)*�construction�(47)
Note.�Figures�in�parentheses�denote�the�number�of�texts�in�which�the�words�were�found�to�be�key.��Italics�=�technical�vocabulary.��*�Can�signal�either�the�problem�or�the�solution�element.
48� Corpus-based�Analyses�of�Problem-Solution�Pattern�
situations.�As�pointed�out�previously,�sometimes�it�is�only�from�the�context�that�we�can�tell�whether�an�item�evokes�a�positive�or�negative�semantic�prosody.�For�example,�landfill�can�refer�to�a�proposed�solution,�as�signalled�by�…�it is�proposed … in�the�example�below:
In order to provide a continuous waste disposal outlet, it is proposed that 3.2 million cu metres of landfill space should be provided.
Later,� in� the� same� text,� though,� this� proposed� solution� is� seen� as� generating� a�problem,�signalled�by�the�use�of�impacts:
The major impacts due to waste deposition at the landfill will be additional leachate and landfill gas that would be generated.
Here,�we�have�an�example�of�what�Hoey�terms�‘progressive�multilayering’�where�the�Response,�i.e.�to�build�a�landfill�site,�only�solves�part�of�the�problem�as�it�sets�up�another�problem�to�be�solved,�i.e.�the�management�of�leachate�and�landfill�gas�(see�Chapter�1).
Lexical�signals:�Technical�vs.�sub-technical�
The�Inscribed�and�Evoking�signals�also�interface�with�the�categories�of�technical�and�sub-technical�vocabulary.�The�majority�of�the�Inscribed�signals�in�Table�4-1�can�be�classified�as�sub-technical�vocabulary�as�they�have�a�discourse-organising�function.�However,�I�have�also�made�the�point�that�in�cases�where�certain�lexis�collocates�to�form�fixed�phrases,�these�should�be�regarded�as�technical�vocabulary�as� they�are�specialised�terms� in� the�field�and�do�not�occur� in�general� language�even�though�each�separate�word�of�a�combination�might�occur�in�general�English.�Impact Assessment�and�Mitigation measures�are�examples�of�such�lexis.�From�Table�4-1�it�can�be�seen�that�the�lexis�in�these�two�phrases�occurs�as�key�in�a�large�number� of� reports,� thereby� delineating� these� words� as� the� main� superordinate�lexis�for�the�Problem�and�Solution�elements�of�the�pattern.�
None�of� the� technical�vocabulary� in�STUCORP�(see�Appendix�4-5)� relates�to�the�Problem-Solution�pattern,�whereas�many�of�the�items�in�PROFCORP�(see�Appendix�4-4)�do.�In�Table�4-2�the�technical�vocabulary�in�PROFCORP�is�indi-cated�in�italics�and�always�occurs�in�the�Problem�slot.�As�pointed�out�earlier,�at�first�glance,�the�remaining�words�would�be�classified�as�sub-technical�according�to�my�definition,�as�they�are�also�used�in�general�English.�However,�if�we�compare�these�Evoking�items�with�the�technical�vocabulary�listed�in�Appendix�4-4,�we�find�a�considerable�overlap�between�the�individual�items�in�Table�4-2�and�their�occur-rence�as�part�of�an�abbreviated�term.�For�example,�waste,�noise,�sewerage�and�ordinance are� found�under� section�B� ‘Environmental� rules�and� regulations’� in�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 49
Appendix� 4-4.� Another� observation� is� that� whereas� sewage and� leachate have�been�categorised�as�Problem�in�Table�4-2�on�the�basis�of�their�inherent�negative�connotation,� they� fall�under�F� ‘Mitigation�Measures’� in�Appendix�4-4�(Sewage Treatment Works, STW, and�Leachate Treatment Facility, LTF) on�account�of�their�combination�with�other�lexis.�Further�investigation�at�sentence�level�is�there-fore�necessary� to�shed� light�on�under�what�circumstances�such� lexis�should�be�classified�as�technical�or�sub-technical.�
It�is�to�be�noted�from�Appendix�4-4�that�a�substantial�proportion�of�the�tech-nical�vocabulary�in�PROFCORP�only�occurs�as�key�in�one�or�two�reports�and�is�therefore�not�recorded�in�Tables�4-1�and�4-2.�One�might�assume�from�the�data�that�this�technical�vocabulary�is�particular�to�a�handful�of�reports�which�could�be�from�the�same�company.�However,�this�was�found�not�to�be�the�case�with�the�ma-jority�of�the�low�frequency�key-key�word�terms.�Concord was�used�to�determine�from�which�file�the�vocabulary�was�extracted.�For�example,�AQO (Air Quality Objectives) in�section�B�‘Environmental�rules�and�regulations’�in�Appendix�4-4�is�only�found�to�be�key�in�two�reports,�but�in�fact�the�49�occurrences�of�this�term�are�spread�over�6�different�reports�which�are�from�over�half�of�the�major�companies�supplying�these�reports.�This�technical�vocabulary�can�therefore�be�considered�as�fairly�representative�of�the�field�on�the�whole�although�it�might�only�occur�as�key�in�a�few�reports.
Differences between PROFCORP and STUCORP
In�general,�the�key�words�and�key-key�words�of�Inscribed�and�Evoking�items�mir-ror�the�pattern�uncovered�in�the�first�stage�of�the�analysis,�i.e.�the�frequency�analy-sis,�thus�providing�ample�evidence�for�designating�both�corpora�as�Problem-Solu-tion�based.�As�for�the�Inscribed�items,�these�were�found�in�both�STUCORP�and�PROFCORP�with�a�sole�focus�on�the�Solution�element�in�PROFCORP,�as�shown�in�Table�4-1.�By�contrast,�a�range�of�Evoking�items�were�found�in�PROFCORP�for�both�elements�of�the�pattern�with�a�focus�on�the�Problem�element.�IN�STUCORP,�though,�no�Evoking� items�were�present� for� the�Solution�element�and�only�one�for�the�Problem�element�(please�refer�to�Table�4-2).�These�differences�can�be�ac-counted�for�by�both�the�type�of�task�and�the�apprenticeship�nature�of�the�Student�Corpus,�as�discussed�below.�
One�of�the�main�reasons�for�this�discrepancy�lies�in�the�composition�of�the�two� corpora.� The� PROFCORP� reports� are� relatively� homogeneous� in� terms� of�subject�matter,�as�they�are�all�environmental�audit�reports�and�we�would�expect�the�same�Inscribed�and�Evoking�lexis�to�occur�across�reports,�which�has�indeed�been�shown�to�be�the�case.�In�contrast,�the�STUCORP�reports�cover�several�dif-
50� Corpus-based�Analyses�of�Problem-Solution�Pattern�
ferent�topic�areas,�e.g.�computer�facilities,�sports�programmes,�language�courses,�credit�card�usage,�academic�dishonesty,�laboratory�security�(see�Appendix�2-2).�The�key�word�vocabulary�is�therefore�much�more�diffuse,�as�reflected�by�the�wider�range�of�topics,�which�means�fewer�occurrences�of�key�words�across�reports.�
However,�some�of�the�differences�could�also�be�accounted�for�by�the�linguistic�competence�of�professional�compared�with�less�experienced�or�apprentice�writ-ers,�as�evidenced�by�the�limitations�of�vocabulary�in�STUCORP�for�expressing�the�Problem-Solution�pattern.�Whereas�the�pattern�is�represented�multi-lexically�in�PROFCORP,�i.e.�through�Inscribed�(which�overlaps�with�sub-technical),�Evoking�and�technical�lexis,�it�is�represented�almost�exclusively�uni-lexically�in�STUCORP,�i.e.�only�through�Inscribed�signals.
Tables� 4-1� and� 4-2� show� that� the� reports� in� STUCORP� employ� the� basic�metalanguage�of�the�pattern,�which�can,�in�part,�probably�be�traced�back�to�the�rubrics�for�the�assignment�(see�Chapter�3�and�Appendix�3-2).�These�stipulate�that�students� are� required� to� investigate� a� problem� or� need,� propose� solutions� and�evaluate�the�feasibility�of�implementing�these�solutions.�It�appears,�therefore,�that�students�are�incorporating�this�metalanguage�provided�in�the�assignment�guide-lines�into�their�project�reports,�with�problem�being�a�salient�key-key�word.�(Here,�I�use�‘salient’�in�the�sense�of�‘an�important�feature�to�note’,�although�problem�is�much�less�statistically�prominent�than�some�of�the�other�key-key�words�in�Table�4-1).�In�fact,�problem�and�need�were�also�found�to�occur�in�the�100�most�frequent�words�in�STUCORP�and�problem was�noted�as�the�only�word�relating�to�the�pat-tern�which�occurred�as�key�in�the�corpus�taken�as�a�whole.�
And�how�can�we�explain�the�paucity�of�key-key�word�Evoking�items�in�STU-CORP?�There�is�only�one�Evoking�item�for�the�Problem�element�in�STUCORP�i.e.�stolen,�whereas�in�PROFCORP�there�are�26�items�which�are�with�one�excep-tion�all�nouns�One�possible�reason�is�that�students�are�operating�within�a�narrow�lexical� range�and�may�tend�to� fall�back�on�using� the�superordinate�category�of�Inscribed�signals�such�as�problem because�they�lack�a�more�sophisticated�reper-toire�of�Evoking�lexis�for�realising�the�Problem�and�Solution�elements.�Another�explanation,� though,� is� that� as� the� topics� of� the� student� reports� cover� a� much�wider�subject�range,�and�consequently�would�have�their�own�Evoking�lexis,�the�same�vocabulary�items�would�not�occur�across�reports�and�therefore�would�not�show�up�as�key�in�four�or�more�of�the�reports.�Nevertheless,�there�are�8,724�differ-ent�types�in�PROFCORP�with�7,268�in�STUCORP,�suggesting�that�students�may�be�over-relying�on�the�metalanguage�exposed�by�the�keyword�Inscribed�signals.�Chapters�6�and�7�shed�more�light�on�this�issue�in�the�examination�of�phraseolo-gies�for�selected�key�words.�
� Chapter�4.� Frequency,�key�word�and�key-key�word�analysis� 51
Conclusion
This�chapter�has�identified�the�high�frequency,�key�word�and�key-key�word�lexis�in�PROFCORP�and�STUCORP�and�demonstrated,�via�internal�linguistic�criteria,�that�both�corpora�can�indeed�be�classified�as�Problem-Solution�oriented.�In�fact,�the�tabulated�findings�can�be�regarded�as�a�form�of�outline�of�the�rhetorical�struc-ture�of�the�reports.�For�example,�the�Inscribed�signals�in�Table�4-1�constitute�the�outline�format�for�STUCORP,�whereas�in�PROFCORP�the�outline�for�the�pattern�is�mainly�highlighted�by�the�Evoking�items�for�the�Problem,�but�Inscribed�lexis�for�the�Solution�element�(see�Tables�4-1�and�4-2).�The�Problem-Solution�pattern�is� further�reinforced�in�PROFCORP�by�the�key�technical�vocabulary�presented�in�Appendix�4-4.�For�instance,�the�technical�items�under�section�E�‘Gases�/�met-als� causing�environmental�damage’� and�under� sections�B� ‘Environmental� rules�and�regulations’�and�F�‘Mitigation�measures’�signal�the�Problem�and�Solution�ele-ments,�respectively,�which�intersect�with�the�Inscribed�and�Evoking�items�in�this�corpus.�
It�has�also�been�put� forward� that� the� linguistic�evidence�drawn�from�these�two�corpora�can�(tentatively)�lead�us�to�distinguish�experienced�writers�from�less�experienced�or�apprentice�writers�on�account�of�the�narrow�range�and�paucity�of�Inscribed�and�Evoking�items�in�STUCORP,�although�more�evidence�from�a�phra-seological�perspective�is�needed�to�substantiate�this�observation.�This�is�the�focus�of�the�following�chapters.
chapter�5
PROFCORPPhraseological�analysis�of�signals��for�the�Problem�element
In�Chapter�4�I�identified�the�Inscribed�and�Evoking�keywords�for�the�Problem-So-lution�pattern�in�PROFCORP�and�STUCORP�(Tables�4-1�and�4-2).�In�this�chap-ter,� I� analyse� selected� items� for� the� Problem� element� in� PROFCORP� from� the�perspective�of�two�broad�categories:
– Causal�semantic�relations�and�non-causal�phrases– Lexico-grammatical�patterns
Examining�lexical�items�realising�the�Problem-Solution�elements�within�a�frame-work�of�semantic�relations�will�set�the�phraseological�analysis�at�a�more�discourse-based�level,�an�overall�aim�which�was�stated�in�Chapter�2.�I�also�rely�on�other�Hal-lidayan�categories,�namely�Theme/Rheme�patterning� to� shed� light�on� the�data,�and�also�look�at�whether�recurrent�patternings�can�reveal�the�discursive�practices�of� this� quite� conventionalised� written� genre� of� EIA� reports,� also� mentioned� as�broad�aims�in�Chapter�2.
A� more� detailed� overview� of� these� two� strands� of� the� classificatory� frame-work,�together�with�some�preliminary�brief�examples�from�PROFCORP�to�illus-trate� the�various� investigative�procedures,� is�outlined�below.� I�also�use� the�Ap-plied�Science�component�of�the�BNC�(approximately�7�million�words;�see�Aston�&�Burnard�1998;�Burnard�2002),�which�is�the�closest�large-scale�reference�corpus�in�terms�of�subject�matter�to�the�EIA�specialised�corpus,�in�order�to�substantiate�some�of�the�findings.
Classificatory framework: Causal semantic relations
In�spite�of�Swales’� reservations�about�corpus-based�methodologies� reviewed� in�Chapter�2,�this�proposed�framework�is�an�attempt�to�go�some�way�towards�achiev-ing�a�‘symbiosis’�between�more�‘top-down’�and�‘bottom-up’�approaches.�
54� Corpus-based�Analyses�of�Problem-Solution�Pattern�
The�brief�corpus-based�analyses�in�Chapter�1�provided�preliminary�evidence�that�problem�statements�are�commonly�found�in�some�type�of�causal�clause�rela-tion.�Halliday�and�Hasan�(1976:�256–261)�discuss� three�specific� types�of�causal�relation�(Reason,�Result�and�Purpose)�and�also�Conditional�relations�under�the�general�causal�relation.�These�all�belong�to�Halliday�and�Hasan’s�conjunctive�re-lation,�one�of�the�four�types�of�relations�for�creating�cohesion�in�text.�A�similar�framework�for�signalling�general�semantic�relations�of�cause–effect�has�been�put�forward�by�Crombie�(1985),�who�proposes�the�following�specific�categories�expli-cated�below:�
� � Reason�–�Result� � � � B�the�result�of�A� � Means�–�Result� � � � B�by�means�of�A� � Grounds�–�Conclusion� � B�deduced�from�A� � Means�–�Purpose� � � A�in�order�to�B� � Condition�–�Consequence� B�would�result�if�A
Some�corpus-based�work�has�already�been�carried�out�using�the�above�semantic�framework�(Flowerdew�1998b)�comparing�various�explicit�linguistic�devices�for�expressing� the� three� semantic� relations� of� reason� –� result;� means� –� result;� and�grounds�–�conclusion�in�a�40,000�word�corpus�of�undergraduate�academic�writ-ing�with�a�comparable�corpus�of�expert�writing,�Global Warming: The Greenpeace Report,� one� of� the� mini-corpora� in� the� MicroConcord� Academic� Corpus� Col-lection.�Marco�(1999)�also�touches�on�this�area�in�her�corpus-based�research�on�the�lexical�signalling�of,�what�she�terms,�conceptual�relations.�She�notes�that�the�cause-result�relation�is�often�realised�by�the�nominal�phrase� the result of.�
Although�I�am�proposing�to�use�a�similar�classificatory�framework�to�the�one�in�my�1998b�study,�my�focus�is�different.�In�this�study,�I�am�concerned�with�how�the�signals�for�the�Problem-Solution�pattern�operate�lexico-grammatically�within�this�framework�rather�than�simply�looking�at�how�these�relations�are�realised�lin-guistically.�The�lexico-grammatical�patterning�for�these�relations�is�analysed,�but�in�relation�to�the�signals�within�the�various�semantic�categories.�Below�I�give�an�example�of�some�typical�phrases�for�the�signal�problem in�PROFCORP�for�Crom-bie’s�five�categories�within�the�general�semantic�relation�of�cause-effect.
� � Reason�–�Result� � � � … export scheme will create a noise problem.� � Means�–�Result� � � � … thereby averting an odour problem� � Grounds�–�Conclusion� � … and so flooding is not a serious problem.�� � Means�–�Purpose� � � …in order to alleviate the problem of..� � Condition�–�Consequence� … If there is a problem with …
It�can�therefore�be�seen�that,�at�the�highest�level,�I�am�adopting�a�notional�(also�sometimes�referred�to�as�‘conceptual’)�classificatory�framework�for�the�phrases�in�
� Chapter�5.� PROFCORP:�Problem�element� 55
which�the�signals�for�the�Problem�element�occur.�Those�phrases�which�cannot�be�assigned�to�one�of�the�causal�categories�above�will�be�analysed�according�to�their�function�in�the�discourse.�The�following�sub-section�will�outline�the�procedures�for�delineating�the�lexico-grammatical�patterning�of�this�Inscribed�and�Evoking�vocabulary.
Classificatory framework: Lexico-grammatical patterns
In�the�corpus-based�approach,�one�key�issue�is�whether�the�point�of�entry�to�in-vestigation�is�with�the�pattern�grammar�or�the�lexis.�
Sinclair’s�work� is�based�on�possibly� two�contradictory�methodologies.�One� in-volves�the�researcher�painstakingly�investigating�the�phraseology�of�one�lexical�item�after�another.�The�other�involves�the�use�of�a�computer�to�list�the�most�fre-quently-occurring�word�sequences.�Francis�faces�the�same�problem�of�whether�to�take�the�lexical�item�as�the�starting�point�or�whether�to�take�the�patterns�as�the�starting�point.�She�investigates�the�adjective,�possible,�for�example,�and�notes�that�it�occurs�with�an�unusually�wide�range�of�patterns,�each�of�which�it�shares�with�other�adjectives.�On�the�other�hand,�she�investigates�patterns�such�as�the�apposi-tive�that-clause,�and�lists�the�nouns�which�share�that�pattern. (Hunston�&�Francis�2000:�31)�
Whether�the�pattern�grammar�or�the�lexis�is�used�as�an�entry�point�would�very�much� seem� to�depend�on� the�purpose�and� scope�of� the� investigation�and�also�the�nature�of�the�corpus.�For�compiling�a�comprehensive�lexico-grammar�of�the�English�language�it�may�be�best�to�start�with�the�pattern�and�identify�all�the�words�that�have�a�particular�pattern.�However,�where�the�aim�is�to�examine�the�lexico-grammar� in� a� specialized� corpus,� it� may� be� more� opportune� to� start� with� the�lexis.�As�Sinclair�(2005)�notes�‘the�characteristic�vocabulary�of�the�special�area�is�prominently�featured�in�the�vocabulary�lists’�(see�Chapter�3).�This�is�indeed�what�the�analyses�in�Chapter�4�have�revealed,�the�key�lexis�for�defining�the�Problem-Solution� pattern,� thus� providing� a� justification� for� using� the� lexis� as� a� starting�point�for�analysis.
At�the�primary�level�of�delicacy�I�first�examine�the�collocational�patterning�of�selected�items�for�the�Problem�element.�In�Chapter�1�the�concordance�for�pollu-tion showed�it�to�collocate�with�‘minimise’.�Another�way�of�looking�at�collocation�is�to�start�with�the�verb�and�then�move�to�the�noun,�which�then�raises�the�question�of�a�word’s�semantic�prosody.�As�Stubbs�(2001b)�points�out,�CAUSE�collocates�with�words�with�unpleasant�connotations,�e.g.�problem, damage, death, disease,�whereas�PROVIDE�collocates�with�words�with�positive�semantic�prosody�such�
56� Corpus-based�Analyses�of�Problem-Solution�Pattern�
as�aid, care, employment�and�facilities.�In�contrast�to�Stubbs,�my�starting�point�is�with�the� lexical� item�working�back�to� investigate�which�verbs�an�item�typically�collocates�with.�This�procedure�has�the�advantage�of�throwing�up�a�range�of�verbs�which�collocate�with�these�evaluative�items.�For�example,�in�PROFCORP,�prob-lem not�only�collocates�with�the�verb�cause*,�but�also�create*,�present* and�pose*.�Concordancing�separately�on� these�verbs�would�show�whether� they� tend�to�be�associated�with�a�negative�or�positive�semantic�prosody.�
By�way�of� a�brief� example,�using� the�core�written�component�of� the�BNC,�I� found,� like� Stubbs,� that� cause* overwhelmingly� collocates� with� nouns� with� a�negative�semantic�prosody,�and�also�often�occurs�in�the�syntactic�structure�verb�+�adjective�+�noun�(e.g.�…�would cause considerable water damage …).�Where�pose*�and�present*�have�the�meaning�of�cause,�the�15�and�16�examples�respectively�are�all�associated�with�nouns�with�a�negative�connotation.�However,�pose*�and�present*�are�unlike�cause*�in�two�respects.�First,�they�usually�collocate�with�nouns�such�as�problem, difficulties, danger,�which�are�Inscribed�signals�for�the�Problem�element,�whereas�cause*�is�more�likely�to�occur�with�Evoking�nouns�(e.g.�your exhibition is�likely to cause traffic congestion�…).�A�unique�feature�of�present*,�however,�is�that�in�seven�out�of�the�16�occurrences�present* is�used�with�a�noun�whose�negative�import�is�neutralised,�e.g.�This will present no difficulties during your holiday.�Cre-ate*,�on�the�other�hand,�is�used�more�often�with�words�with�pleasant�associations,�e.g.�Water bubbles up through the pebbles, creating a cool refreshing effect.�This�brief�analysis� of� causative� verbs� shows� that� words� seemingly� belonging� to� the� same�semantic�set�do,�in�fact,�have�different�collocational�behaviours�not�only�in�terms�of�their�semantic�prosodies,�but�also�semantic�preferences�for�either�Inscribed�or�Evoking�items�(semantic�preference�is�usually�used�to�refer�to�the�nature�of�the�noun,�whether�it�be�concrete�or�abstract,�or�belonging�to�a�certain�semantic�field,�e.g.�disease�etc.;�see�Partington�2004).
Another� objective� is� to� investigate� the� grammatical� company� that� a� word�keeps�(see�Chapter�1).�With�respect�to�the�collocates�of�the�item�under�investiga-tion,�we�could�also�consider�the�grammatical�company�that�a�collocation�keeps,�which�could�be�viewed�as�lexical colligation.�For�example,�it�has�been�noted�that�in� PROFCORP� problem collocates� with� the� verbs� cause*,� create*,� present*� and�pose*.�The�next�step�would�be�to�consider�the�tense,�voice�and�aspect,�i.e.�the�col-ligational�preferences,�of�the�verbs�in�these�verb�+�noun�collocations.�
Apart� from�the�usual� features�considered� in�a�phraseological�approach,� i.e.�collocation,�colligation,�semantic�prosody�and�semantic�preference,�another�con-sideration� relates� to� the� interpersonal� dimension.� It� is� also� important� to� know�whether� the� main� verb� is� marked� interpersonally� either� with� a� modal� verb� or�some�other�expression�such�as�unlikely�or�probably to�indicate�epistemic�use.�An-other�point�of�interest�is�whether�different�forms�of�a�lemma,�e.g.�problem�and�
� Chapter�5.� PROFCORP:�Problem�element� 57
problems pattern�differently,�not�only�in�terms�of�their�lexico-grammatical�pat-terning�but�also�in�terms�of�their�distribution�across�the�five�causal�categories�and�in�any�non-causal�phrases.�It�may�also�be�the�case�that�certain�phrases�are�associ-ated�with�a�particular�Theme/Rheme�position�in�the�clause�or�sentence�within�a�particular�causal�category.�
All�these�aspects�will�be�considered�in�the�analyses�below�of�selected�items�from�PROFCORP:�the�Inscribed�signals�problem, problems, need,�and�the�Evok-ing�items�impact�and�impacts.
Analysis of problem and problems
The� Inscribed� items� problem� and� problems� do� not� occur� as� key-key� words� in�PROFCORP�(see�Table�4-1).�However,�as�problem�and�problems�occurred�as�key-key� words� in� STUCORP,� it� was� decided� to� examine� these� in� PROFCORP� as� a�point�of�comparison.�
Table�5-1�below�presents�a�summary�of�the�findings�for�problem�and�prob-lems in�PROFCORP,�based�on�the�classificatory�framework�outlined�in�the�pre-vious�section.�The�tokens�in�each�causal�category�refer�to�the�number�of�tokens�in�the�text;�the�tokens�where�either�problem�or�problems�occur�as�a�heading�or�sub-heading�have�been�excluded�from�the�table�below.�
Causal�categories�for�problem
It�can�be�seen� from�the�above� table� that� the�majority� (29)�of� the�41� tokens� for�problem in�PROFCORP�fall� into� the�category�of�Reason-Result,�which�can�be�
Table 5-1. In-text�tokens�for�problem�and�problems�in�PROFCORP�
PROFCORPProblem Problems
(SUB)-HEADING � 0 � 0CAUSAL RELATIONReason-Result 29 20Means-Result � 2 � 0Grounds-Conclusion � 1 � 3Means-Purpose � 6 10Condition-Consequence � 1 � 2Total�(causal) 39 35Non-causal � 2 16Overall Total (In-text) 41 51
58� Corpus-based�Analyses�of�Problem-Solution�Pattern�
viewed�either�from�the�perspective�of�cause/reason,�i.e.�what�problem�is�caused,�or�result/effect,�i.e.�what�is�the�cause�of�the�problem.�However,�under�Reason-Re-sult,�I�also�consider�what�is�offered�as�the�solution�to�a�potential�problem�as�this�is�also�a�type�of�causation�as�explained�below.
Out� of� the� 29� tokens� of� problem� in� a� Reason-Result� relation,� 25� of� these�can� be� classified� as� cause/reason,� i.e.� what� problem� is� caused.� This� concept� is�overwhelmingly� expressed� via� explicit� and� implicit� causative� verbs� rather� than�through�other�linguistic�devices�such�as�complex�prepositions�[e.g.�due to�(1),�be-cause of�(1)�or�nouns,�e.g.�cause (1)].�The�explicit�causative�verbs�collocating�with�problem are�create (4),�cause (2),�pose (2),�present�(1),�become�(1).�These�verbs�are�invariably�in�the�active�voice�with�a�variety�of�modal�auxiliaries�used�to�indicate�a�possible�future�problem�arising,�e.g.:
� � …works at the tunnel portal will create a noise problem.
There�are�six�tokens�of�problem which�occur�with�implicit�causative�verbs�(mi-nimise, alleviate, eliminate, avert, resolve,� address).� Implict� causative� verbs� are�defined�by�Fang�and�Kennedy�(1992:�65)�as� ‘those�which�entail� the�meaning�of�‘…make�somebody/thing�do�something’�or�‘make�somebody/thing�+�adj.’�The�five�verbs�in�this�context�can�all�be�roughly�paraphrased�as�‘make�the�problem�better’�in� some� way.� For� example,� minimise� in� the� phrase� …should minimise much of the problem, can�be�paraphrased�as�‘make�[the�problem]�less�severe’.�In�one�case�the�construction�‘adverb�+�ing’�was�used,�e.g.�…thereby averting an odour prob-lem.�These�data�thus�reveal�that�when�problem collocates�with�explicit�causative�verbs,�these�have�a�negative�semantic�prosody.�However,�when�problem collocates�with�implicit�causative�verbs,�these�take�on�a�positive�semantic�prosody�(see�Louw�1993;�Stubbs�2001c),�acting�as�a�two-way�signal�for�the�Problem-Solution�pattern�(Hoey�1983).
Problem� was� also� found� to� collocate� with� various� parts� of� the� verb� ‘be’� in�six�phrases.�Here,� ‘be�a�problem’� is�found�to�combine�with�some�of�the�techni-cal�keyword�Evoking�lexis�for�the�Problem�element,�e.g.�leachate,�listed�in�Table�4-2.�Reference� is�made� to�a� specific�existing�problem�in� the�Theme�part�of� the�sentence,�which�constitutes�the�‘point�of�the�departure�of�the�message’�(Halliday�1994).�Problem� always�occupies�Rheme�position,� i.e.� the� rest�of� the�message.� I�would�like�to�argue�that�in�the�context�of�these�environmental�reports�‘be’�takes�on�the�semantics�of�a�causative�verb�rather�than�acting�as�a�stative�verb,�as�it�im-plicitly�means�that�a�present�problem�could�create�a�future�one.�To�illustrate,�‘be’�could�well�be�substituted�by�an�explicit�causative�verb,�such�as�‘pose’�or�‘present’�in�all�the�examples�below:�
…it is considered unlikely that septicity would be a problem.
� Chapter�5.� PROFCORP:�Problem�element� 59
Increased noise levels are not expected to be a problem.
…and operational noise is not considered to be a problem
When�problem occurs�with�existential�‘there’,�as�in�the�two�examples�below,�‘be’�also�seems�to�be�acting�as�an�event�verb,�but�in�this�case�as�a�result/effect�verb�as�it�has�the�meaning�of�‘arise’.
…there should not be any disposal problem.
…it will be unlikely that there will be a problem…
This�use�of�‘be’�with�problem�is�also�evident�in�the�Condition-Consequence�re-lation,�although�there�is�only�one�occurrence�of�this�causal�relation:��If there is a problem with …
It�is�interesting�to�note�that�in�addition�to�the�two�tokens�of�problem�occur-ring�with�existential�‘there’�which�co-occur�with�‘be’�acting�as�a�result/effect�verb,�only�two�other�of�the�29�tokens�for�problem collocate�with�verbs�signalling�result/effect (e.g.�generate,�derive),�rather�than�with�cause/reason�verbs,�e.g.:
The problem derives primarily from degradation of…
The�tokens�of�problem in�both�the�relations�of�Means-Purpose (6� tokens)�and�Means-Result� (2� tokens)� collocate� with� implicit� causative� verbs� (e.g.� improve,�ameliorate, alleviate, mitigate, prevent, avert,�solve)�which,�as�shown�above,�signal�the� Solution� element,� thereby� according� them� the� status� of� two-way� signposts�(e.g….�by solving the problem of… for� the�Means-Result� relation).�One� impor-tant�observation�concerning�these�two-way�signals�is�that�they�tend�to�occur�in�the�same�lexico-grammatical�environment�as�the�Evoking�keywords�(e.g.�noise,�odour)�for�the�Problem�element�(see�Chapter�4,�Table�4-2),�e.g.:
� To ameliorate the future traffic noise problem, a package of …
…all solid materials removed to prevent an odour problem.
These�findings�on�explicit�and�implicit�causative�verbs�are�thus�in�keeping�with�other�research�on�cause-effect�markers�where�the�use�of�causative�verbs�far�out-weighed�‘result’�verbs,�both�in�terms�of�types�and�the�number�of�tokens�(Flow-erdew�1998b).
Non-causal�categories�for�problem
There�are�only�2�tokens�for�problem�which�do�not�fall�into�one�of�the�causal�cat-egories.�These�could�be�viewed�as�having�the�status�of� ‘evaluating�the�problem’,�
60� Corpus-based�Analyses�of�Problem-Solution�Pattern�
but� in�a�positive�sense,�e.g.�…no insurmountable problem to the water supply is envisaged.
In�the�following�section,�I�will�analyse�the�causation�and�non-causation�based�tokens�for�problems in�PROFCORP�which�are�also�itemised�in�Table�5-1�above.�I�will�also�compare�problem�and�problems to�see�to�what�extent�different�forms�of�a�lemma�pattern�in�a�similar�fashion�or�differently�in�professional�writing.�
Causal�categories�for�problems
There�are�many�similarities�between�the�distribution�of�the�tokens�for�problems�and�problem in�PROFCORP�across�the�five�semantic�causal�categories.�First,�the�majority�of�these�tokens�(18�out�of�20)�in�the�Reason-Result�category�combine�with� causative� verbs.� Only� three� of� these� [18]� tokens� collocate� with� verbs� sig-nalling�result/effect�(e.g.�Pollution problems could�occur…;�Where potential prob-lems may arise�…),�while�the�remainder�are�divided�between�explicit�and�implicit�causative�verbs.�The�explicit�verbs�for�cause/reason�occurring�with�problems are�cause�(4)�result in�(4)�create,�and�(1)�present�(1).�Modals�and�other�mitigating�ex-pressions,�similar�to�those�occurring�with�problem,�are�used�to�signal�that�these�problems,�in�the�main,�refer�to�possible�ones�arising�from�planned�construction�work,�e.g.:
…that could cause odour and potential health problems.
…are not expected to result in significant odour problems.
One�difference,�however,�lies�in�the�choice�of�verbs�with�problem�and�problems.�‘Be’� in� the� sense� of� ‘create’� occurred� with� problem� (e.g.� …� are not expected to be a problem),�but�did�not�occur�with�problems.�These�data�suggest�that�certain�causative�verbs�may�prefer�singular�or�plural�nouns,�or�indeed�have�a�tendency�to�collocate�with�premodified�nouns,�and�that�factors�such�as�these�have�a�bearing�on�verb�+�noun�collocation�which�are�not�considered�in�collocational�dictionaries.�The�string�‘be�a�problem/problems’�was�searched�in�the�Applied�Science�written�domain�of�the�BNC�and�it�was�found�that�there�were�26�instances�out�of�30�where�‘be’�had�the�meaning�of�‘create’�(e.g.�… only a few faces are supplied and this may be a problem for anyone running wordprocessing…).� In� contrast,� this� construc-tion�was�not�found�in�the�15�instances�of�‘be�problems’,�i.e.�with�the�lemma�in�the�plural,�where�‘be’�was�always�found�with�existential�‘there’�having�the�meaning�of�‘arise’�(e.g.�there might well be problems with klystrons…).
Although�the�tokens�for�problems�in�the�Means-Purpose�relation�collocate�with� similar�verbs� to� those� for�problem in�PROFCORP,�and�also�cover�a� large�range�of�verbs�(e.g.�address, reduce,�resolve, deal with, remedy, minimise, prevent,
� Chapter�5.� PROFCORP:�Problem�element� 61
overcome, solve),�the�Theme/Rheme�patterning�is�different.�Whereas�three�out�of�the�six�tokens�for�problem�occur�in�Theme�position,�all�the�tokens�for�problems�occur�in�Rheme�position.�This�is�no�doubt�because�of�the�long�clauses�postmodi-fying�problems,�or�a�following�subordinate�clause�as�in�the�examples�below.
� � … detailed design to remedy the noise problems identified prior to the construc-tion of..
…will be required to overcome the anticipated traffic problems on Lung Mun Road and the junction..
…has been drawn up to address the potential main problems identified above, so that …
In�fact,�one�key�difference�between�the�tokens�for�problem�and�problems�is�that�problems is�usually�premodified�across�all�causal�categories�(32�out�of�35�cases)�and�also�has�various�forms�of�postmodification�in�the�Means-Purpose�category�outlined�above.�It�was�found�that�problem was�premodified�in�18�out�of�39�cases,�usually�when�it�occurred�in�the�Means-Purpose�relation,�and�on�the�few�occa-sions�when�it�was�postmodified,�this�postmodification�was�in�a�brief�prepositional�phrase,�e.g.�The problem of leachate�….�
Non-causal�categories�for�problems
The�remaining�16�tokens�for�problems which�cannot�be�classified�under�any�of�the�causation�categories�are� in�sentences�where� the�main� function� is� to�denote�the�existence�of�the�problem�as�in�the�examples�below.�And�once�again,�we�find�a�combination�of�problems with�technical�Evoking�lexis,�e.g.�groundwater:
The same problems exist with mobile cooling units.
The factory workers have at times identified problems with groundwater …
In�the�following�section�I�will�analyse�the�lexico-grammatical�patterning�for�the�noun� need,� which� like� problem� and� problems,� also� explicitly� signals� a� negative�evaluation,�although�it�was�not�found�as�a�key�word�in�PROFCORP.�I�will�focus�on�the�nominal�form�only�so�that�the�analysis�is�compatible�with�the�analyses�for�prob-lem�and�problems�and�can�be�carried�out�under�the�same�analytical�framework.
62� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Analysis of need
Causal�categories�for�need
In�PROFCORP�there�are�118�tokens�for�need of�which�62�are�verbal,�53�nominal,�and�another�three�(also�nominal)�acting�as�headings.�Out�of�the�53�tokens�where�need�is�acting�as�a�noun,�14�tokens�(slightly�over�25%)�are�causation-related.�In�these�14�tokens,�explicit�and�implicit�causative�verbs�play�a�prominent�role�in�re-alising�causation.�For�example,�there�are�4�explicit�verbs�in�various�grammatical�patterns,�e.g.:
� � …will, in turn, generate a need and …
This will give rise to the need for…
…leading to the need to review …
The�other�six�phrases�all�employ�an�implicit�verb�signalling�some�kind�of�partial�resolution�of�a�problem,�e.g.�
…will minimise the need to divert clinical waste..
…will avoid the need to dredge and dispose of …
In�two�phrases,�the�pattern�There is …a need to/for…�is�involved�in�a�Grounds-Conclusion relation,�summing�up�a�previous�stretch�of�text,�which�is�signalled�by�therefore:
There is a need, therefore, for a dedicated-purpose built facility.
There is therefore a need to minimize landfill gas emissions.
In�fact,�there�were�seven�other�cases�where�need was�found�in�this�patterning,�four�of�which�were�in�the�negative,�signalling�absence�of�a�need�(which�is�also�encapsu-lated�by�the�pattern�without the need to/for…,�occurring�3�times)�e.g.:
There is no need to employ a specialist contractor.
There was no immediate need for gas extraction.
Now,� these� other� examples� of� this� patterning� with� need are� also� involved� in� a�causation�relationship,�but�are�not�explicitly�signalled�by�a�marker�such�as�there-fore. Although�Crombie�(1985)�mentions�that�there�are�very�few�cases�in�which�Grounds-Conclusion� can� be� inferred,� the� above� examples� indicate� that� there�may�well�be�exceptions.�And,�in�fact,�an�examination�of�the�wider�context�of�these�seven�sentences�reveals�that�three�of�these�do�signal�Grounds-Conclusion,�wrap-
� Chapter�5.� PROFCORP:�Problem�element� 63
ping�up�a�previous�sub-section�of�text�by�proposing�a�solution,�e.g.�There will be a need for irrigation water.
Non-causal�categories�for�need
The�remaining�tokens�for�need in�PROFCORP�(40%�of�the�total)�refer�to�identi-fication�or�establishment�of�a�particular�need,�where�the�attribution�is�usually�to�a� specific�organization�or�previous�documentation.�Another�observation�about�these�examples�is�that�the�verbs�are�usually�in�the�present�perfect�aspect�indicat-ing�that�some�kind�of�problem�exists�which�sets�up�the�conditions�for�a�proposed�solution.�
The EIA has identified the need for a flyover.
In�sum,�like�problem�and�problem,�need has�also�been�found�to�be�involved�in�causation-based�relations,�and�it�remains�to�be�seen�whether�these�items�operate�in�a�similar�way�in�STUCORP�(see�Chapter�7).
Analysis of impacts and impact
In�this�section�I�analyse�the�lexico-grammatical�patterning�of�two�Evoking�items,�impacts and�impact,�in�PROFCORP�and�compare�them�with�the�patterning�for�the�Inscribed�signals�of�problems and�problem.�The�other�Evoking�items�will�not�be�examined�separately�as�many�of�these,�both�the�non-technical�(e.g. noise,�traffic)�and�technical�(e.g.�Dba,�leachate)�lexis,�have�been�found�to�collocate�with�impacts and�impact�(see�Table�4-2�for�a�list�of�these�Evoking�items).�If�the�other�Evoking�items�were�examined,�there�would�be�a�lot�of�unnecessary�repetition;�hence,�this�analysis�of�impacts and�impact also�serves�as�a�template�for�analysis�of�the�other�Evoking�items�in�PROFCORP.�No�analyses�of�Evoking�items�in�STUCORP�will�be�carried�out�as�the�only�key�keyword�Evoking�item,�stolen,�occurs�just�49�times�across�6�different�texts.
Causal�categories�for�impacts
I�begin�the�analysis�with�impacts as�this�item�has�the�highest�frequency�of�key-word�occurrence�among�all�the�Evoking�items�in�PROFCORP,�surfacing�as�a�key-word�in�50�out�of�the�60�reports.�Out�of�a�total�of�991�tokens�for�impacts,�five�are�verbs�and�30�of�these�act�as�headings�or�sub-headings�which�have�been�discount-ed�from�the�following�analysis.�It�is�interesting�to�note�that�when�impacts�occurs�
64� Corpus-based�Analyses�of�Problem-Solution�Pattern�
as�some�type�of�heading�it�is�usually�in�combination�with�other�Evoking�items,�e.g.�‘environmental’,�‘noise’.�Here,�as�I�have�argued�in�Chapter�4,�when�this�type�of�sub-technical�vocabulary�is�in�a�multi-word�unit�it�takes�on�a�technical�meaning.�Of�the�remaining�956�tokens�for�the�noun�impacts,�497�(i.e.�48%)�have�a�causative�function.�A�breakdown�of�these�according�to�the�five�semantic�categories�of�causal�relations�is�given�in�Table�5-2.
In�common�with�the�Inscribed�signals�for�causation,�the�Reason-Result�cat-egory� is� by� far� the� most� prominent,� with� 384� tokens� out� of� the� 497� (i.e.� 77%)�occurring� in� this� class.� In� the� Reason-Result� category,� 12� tokens� for� complex�prepositions�were� found�with� impacts,�with� ‘due� to’�occurring�nine� times.�Out�of�the�15�adverb�tokens�(‘as�a�result’,�‘therefore’,�‘hence’,�‘consequently’,�and�‘thus’)�occurring� with� impacts,� only� five� were� found� to� belong� in� the� Reason-Result�category,�as� ten�of� these�signalled� the�Grounds-Conclusion relation.�However,�it�was�the�explicit�and�implicit�causative�verbs�which�largely�defined�the�Reason-Result�category.�
The�explicit� causative�verbs� signalling� cause/reason�number�75� tokens.�Of�these�cause�and�result in�are�by�far�the�most�common�occurring�31�and�22�times,�respectively�with�impacts.�In�this�patterning,�impacts�had�a�tendency�to�be�pre-modified�by�general�classifiers�(e.g.�environmental,�ecological)�as�in�the�example�below:
Option 1 will result in greater ecological impacts than Option 2.
Because�potential�environmental�impacts�are�being�referred�to,�the�lexico-gram-matical�patterning�for�impacts with�causation�verbs�contains�a�variety�of�mitigat-ing�expressions,�which�are�very�similar�to�those�for�problems�and�problem,�e.g.�
…an access road could result in significant impacts.
… are unlikely to cause any additional environmental impacts to the adjacent environs.
Table 5-2. In-text�tokens�for�impacts�and�impact�in�PROFCORP
Causal relation No. of tokens IMPACTS No. of tokens IMPACT
Reason-Result 384 214Means-Result 13 18Grounds-Conclusion 16 11Means-Purpose 82 51Condition-Consequence 2 2Total�(causal�relations) 497 296Others 459 356Overall total: 956 652
� Chapter�5.� PROFCORP:�Problem�element� 65
Other�explicit�causative�verbs�occurring�with�impacts include:�generate�(5),�cre-ate (5),� lead to�(3)�pose�(2),�give rise to�(2).�What�is�noticeable,�however,�is�that�of�the�three�transitive�causative�verbs�cause,�create�and�generate,�it�is�only�gener-ate�which�is� found�in�the�passive�form,�and�here,�all�five�instances�are� in�some�type�of�passive�construction,�e.g.�Several schools will be subject to road traffic noise impacts�generated from Roads… As�I�have�suggested,�this�is�a�type�of�lexical�col-ligation�(i.e.�the�grammatical�company�that�a�collocation�keeps).�In�the�context�of�these�environmental�reports�impacts�has�a�strong�tendency�to�collocate�with�a�variety�of�explicit�causative�verbs�which�have�a�colligational�preference�for�the�ac-tive�voice�(except�for�generate which favours�the�passive),�as�was�also�found�to�be�the�case�with�verb�+�noun�collocations�of�problem and�problems�in�PROFCORP.�Because�the�collocational�and�colligational�patterning�of impacts�is�so�similar�to�that�of�problems,�this�Evoking�item�seems�to�be�functioning�as�a�covert�synonym�of�problems,�most�likely�because�it�is�a�type�of�sub-technical�vocabulary,�a�lexical�item�used�in�general�English�which�also�takes�on�a�specialised�meaning�in�certain�fields.
I� argued� in� a� previous� section� that� when� problem collocates� with� various�parts�of�the�verb�‘be’�it�takes�on�the�semantics�of�a�causative�verb�as�this�idiomatic�phrase�‘be�a�problem’�(e.g.�it is considered unlikely that septicity would be a prob-lem)�has�the�meaning�of��‘cause�/�create�a�problem’.�Likewise�the�phrase�‘have….�impacts’�also�implies�the�meaning�of�‘cause’�or�‘create’.�There�are�five�instances�of�this�use�in�PROFCORP,�as�exemplified�by�the�phrases�below:
… works for these pipelines will have negligible impacts.
… major site activities which are likely to have noise impacts.
‘Be� a� problem’� and� ‘have� …� impacts’� thus� fit� Sinclair’s� (1991)� idiom� principle,�although� the�examples� in�PROFCORP�demonstrate� that� there� is�more� internal�lexical�variation�in�the�case�of�‘have�…�impacts’.�These�examples�also�provide�evi-dence�for�the�polysemy�of�‘be’�and�‘have’�in�certain�lexical�phrases,�just�like�many�other�words�of� the� language� (see�Moon�1998� for�a�discussion�and�examples�of�polysemy�in�fixed�expressions�and�idioms).
I� will� now� examine� those� implicit� causative� verbs� occurring� with� impacts, which� act� as� two-way� signals� for� both� the� Problem� and� Solution� element.� The�verbs� occurring� with� this� function� are� ‘reduce’,� ‘minimise’,� ‘mitigate’,� ‘prevent’,�‘avoid’,� ‘decrease’� and� ‘control’.�There�are�61� tokens�of� these�verbs� in� total,�with�reduce�and�minimise by�far�the�most�common�occurring�28�and�21�times,�respec-tively,�usually�in�the�active�voice�which�is�very�similar�to�the�colligational�pattern-ing�of�the�explicit�causative�verbs�with�impacts.�However,�we�also�have�another�type�of�patterning�operating�here,�the�combination�of�Inscribed�signal�+�Evoking�
66� Corpus-based�Analyses�of�Problem-Solution�Pattern�
item.�The�two�verbs�reduce�and�minimise�which�explicitly�signal�the�Solution�el-ement,�were� identified�as�keyword� Inscribed� signals� in�PROFCORP�(see�Table��4-1),�and�here�are�found�to�combine�with�the�Evoking�item�impacts.�This�keyword�patterning�is�also�prevalent�in�the�Means-Purpose�and�Means-Result�relations,�as�we�shall�see�later.
It�has�already�been�noted�that�there�are�75�tokens�for�impacts occurring�with�explicit�causative�verbs�such�as�‘cause’,�‘create’,�‘result�in’�etc.,�signalling�the�cause/reason�relation.�The�number�of�tokens�for�impacts found�with�the�other�explicit�verbs�signalling�the�result/effect relation�is�80.�This�is�a�striking�difference�com-pared�to�the�distribution�of�such�verbs�with�problem�and�problems where�only�two�out�of�the�26�tokens�for�problem�and�three�out�of�the�18�tokens�for�problems�collocated� with� verbs� signalling� the� result/effect� rather� than� the� cause/reason�relation.�
An�analysis�of�the�explicit�result/effect�verbs�occurring�with�impacts shows�that�these�are�limited�to�only�three�verbs:�arise from�(54),�result from�(19)�and�oc-cur�(7).�There�are�also�17�cases�where�impacts occurs�with�existential�‘there’,�plus�a�verb�indicating�the�future.�As�I�have�pointed�out�in�the�previous�section,�in�these�cases�‘be’�is�acting�as�an�implicit�result/effect�verb�as�it�has�the�meaning�of�‘arise’:
There will be no adverse visual impacts.
There will be no significant noise impacts during the operational and mainte-nance period.
As�for�the�explicit�result/effect�verbs,�occur�has�a�different�colligational�pattern-ing�from�the�other�two,�arise from�and�result from.�Occur�was�always�found�as�a�finite�verb�(e.g….and no significant impacts will occur),�whereas�both�arise from�and� result� from� also� occur� in� reduced� relative� clauses.� In� this� respect,� occur is�similar�to�‘be’�and�‘have’,�which�are�all�found�in�sentences�where�a�more�general�reference�is�made�to�the�impacts.�In�contrast,�arise from�and�result in�are�found�in�more�complex�clauses�and�sentences�where�more�precise�information�is�given,�as�explained�below.
In�the�case�of�arise from,�30�out�of�the�54�tokens�are�of�the�participial�variety�as�part�of�a�defining�reduced�relative�clause.�These�were�equally�divided�between�the�Theme�and�Rheme�parts�of�the�sentence,�as�in�the�following�examples:
The major impacts arising from the above activities would be seawater quality, noise and dust.
The report addresses all potential environmental impacts arising from construc-tion and operation of the proposed LRWT.
� Chapter�5.� PROFCORP:�Problem�element� 67
The�verb�result from�also�displayed�a�very�similar�colligational�and�Theme/Rheme�patterning.� Out� of� the� 19� occurrences,� 10� were� of� the� form� resulting from,� as�shown�below.�
Land use impacts resulting from the modified master plan configuration are similar to impacts assessed in the New Airport Master Plan EIA.
This form of mitigation would significantly reduce the scale of impacts resulting from the AFRF project.
Although�there�are�80�explicit�verb�tokens�of� three�different� types�(result from,�arise from and�occur)�signalling�the�result/effect�aspect,�there�are�also�89�tokens�of�the�preposition�‘from’�with�impacts.�Here,�‘from’ has�a�very�similar�function�to�these�verbs,�as�it�can�be�considered�as�a�reduction�of�‘arising�from’�or�‘resulting�from’:
� � Potential impacts from road traffic noise have been assessed.
This�causative�function�of�‘from’�is�listed�as�an�entry�in�COBUILD (p.�584,�entry�no.�24),�but�in�the�two�COBUILD�examples�below�from does�not�have�the�same�grammatical�status:
The committee’s enquiry arose from representations made by Basildon district Council.
It’s a spin-off from military and space research.
In�the�first�example�above,�from is�part�of�an�intransitive�phrasal�verb,�whereas�in�the�second�it�is�part�of�a�prepositional�phrase,�a�type�of�reduced�relative�clause�with�a�causative�function�which�is�its�use�in�the�combination�of�impacts�+�‘from’.�Now,�this�causative�use�of� ‘from’�raises�a�question.�Usually,�prepositions�are�re-garded�as�structural�or�grammatical�words,�but� in� this�case� ‘from’�has�a� lexical�rather�than�grammatical�orientation�as�it�operates�more�like�a�content�word.�This�data� therefore� questions� the� polar� divisions� of� words� into� open� class� sets� (like�nouns)�or�closed�class�sets�(like�prepositions).�Another�question�to�ask�is�when�‘from’�is�preferred�to�‘arising�from’�or�‘resulting�from’�–�an�issue�which�has�been�raised�previously.
An�inspection�of�impacts with�resulting from,�arising from�and�from�reveals�that� in�this�grammatical�construction�arising from�or�resulting from�seem�to�be�used�when�the�sentence�structure�adheres�to�the�simple�pattern�of�subject�+�verb�+�complement,�and�the�sentence�itself�is�quite�short,�as�in�the�example�below:
The major impacts arising from the above activities would be seawater quality, noise and dust.
68� Corpus-based�Analyses�of�Problem-Solution�Pattern�
However�from�seems�to�be�preferred�when�either�the�complement�part�of�the�verb�is�complicated�involving�a�succession�of�post-modifying�clauses,�as�in�(a)�below,�or�when� from� is�found�in�a�rankshifted�phrase�within�the�nominal�group,�as�in�(b).�Normally,�we�think�of�contextual�and�situational�features�as�affecting�lexical�choice�within�the�sentence,�but�here�we�appear�to�have�cases�where�the�internal�sentence�grammar�has�a�bearing�on�this�aspect.�
� (a)� Noise impacts from construction activities have been predicted to be within the HKPSG criterion for all unrestricted periods except at the isolated housing at Peng Chau where the limit will be exceeded for a short period of time.
� (b) The potential sources of water quality impacts from the construction of the Plant will be similar to typical land based construction activities which involve construction run-off and ……
Another�phrase�which�is�not�normally�considered�as�signalling�result/effect,�but�which�appears�to�have�this�function�in�this�context�is�associated with,�of�which�there�are�35�instances�in�the�data.�Two�of�these�examples�are�given�below:
Noise impacts associated with traffic serving the barging point are minor and would only increase noise levels marginally.
Water quality impacts associated with construction are therefore likely to be minimal.
Here,� associated with� seems� to� be� somewhat� ambiguously� involved� in� a� causal�effect�and�could�well�be�being�used�euphemistically�in�scientific�writing�as�a�hedg-ing� device,� more� in� the� sense� of� ‘correlated� with’� rather� than� ‘caused� by’,� most�probably� in� line�with� the�discursive�practices�of� this�particular�discourse�com-munity.�In�this�way,�scientists�would�avoid�claiming�a�direct�causal�effect�which�would�forestall�any�challenges�from�their�peers,�especially�when�controversial�is-sues�are�involved�(see�Hyland�1998).�Concordancing�this�phrase�in�the�Applied�Science�domain�of�the�BNC�shows�that�it�occurs�1327�times�in�162�texts.�An�in-vestigation�of�the�concordance�lines�selected�on�a�one�per�text�basis�reveals�that�in�40%�of�cases�it�clearly�has�a�negative�semantic�prosody,�but�one�that�is�probably�attenuated:
The commission is concerned about the possible risks associated with releasing genetically altered organisms….
In�a�paper�on�corpus�semantics,�Stubbs�(2001a)�argues�that�the�conventionalised�view�that�pragmatic�meanings�are�usually�inferred�by�the�reader/listener�may�be�overstated�and�that� large-scale�corpus�studies�can�provide�evidence�to�show�us�that�pragmatic�meanings�can�also�be�conventionally�encoded�in�linguistic�form.�
� Chapter�5.� PROFCORP:�Problem�element� 69
This�may�well�be�the�case�with�‘associated�with’�which�appears�to�have�a�weaker�pragmatic�force�than�other�causative�markers.�See�Flowerdew�(2008d),�who,�based�on�corpus�evidence,�makes�a�case�for�associated with being�classified�as�a�‘mate-rial’,�i.e.�happening�verb,�rather�that�a�‘relational’�one�in�this�specialised�genre.
An� analysis� of� the� PROFCORP� data� of� impacts� in� the� Reason-Result cat-egory�has�shown�that�as� in�common�with�problems,� the�Evoking� item� impacts favours�collocation�with�explicit�and�implicit�verbs�for�both�the�cause/reason�and�result/effect�functions.�One�striking�difference�is�that�impacts�is�accorded�a�much�greater� degree� of� specificity,� as� evidenced� by� its� collocational� and� colligational�patterning�with�arise from,�result from�and�from.�What�is�particularly�noteworthy,�though,�is�that�complex�prepositions�(e.g.�‘due�to’,�‘because�of ’,�‘as�a�result�of ’)�for�cause/reason,� and�adverbs� (e.g.� ‘therefore’,� ‘hence’,� ‘thus’)� for�result/effect�have�been�shown�to�be�much�less�common�than�one�would�have�originally�supposed.
82�tokens�were�recorded�for�the�Means-Purpose�category,�covering�12�differ-ent�verbs,�with�minimise�(29),�mitigate (18)�and�reduce�(16)�occurring�the�most�frequently�with�impacts.�By�far�the�most�common�grammatical�construction�used�to�express�the�Means-Purpose�relation�was�(in�order)�to�+�infinitive;�there�was�only�one�example�of�each�of�the�following�constructions:�‘in�such�a�way�that’,�‘so�that’,�‘so�as�to’�and��‘in�order�that’.�
Moreover,�it�is�interesting�to�note�that�there�were�only�ten�cases�where�a�verb�+�impacts�occupied�Theme�position�in�the�sentence,�as�in�the�example�below:
To mitigate these adverse impacts additional mitigation was incorporated into the design.
In� eight� out� of� these� ten� cases� impacts� was� premodified� by� these.� As� noted� in�Chapter�3,�when�such�nouns�are�premodified�by�determiners�the�anaphora�is�car-ried�by�the�determiner�(in�this�case�these),�and�the�noun�assumes�an�evaluative�function.�In�the�above�example,�adverse therefore�has�an�intensifying�function.
In�contrast,�in�the�majority�of�cases�where�a�nominal�group�containing�im-pacts�occurs�in�Rheme�position,�impacts�was�found�not�to�refer�to�any�specific�entity�within�the�text,�as�in�the�examples�below:
� (a)� The objectives of this supplementary Environmental Impact Assessment (EIA) are summarised as follows: to define….; to identify …….; and to recommend measures to minimise any adverse impacts to within established guidelines and standards.
� (b)� special procedures were recommended for the dredging and disposal of the con-taminated mud to minimise potential impacts.
70� Corpus-based�Analyses�of�Problem-Solution�Pattern�
� (c)� The landscape plans presented with visual assessment form the basis of a com-prehensive landscaping and tree planting programme designed to ameliorate the visual impacts of the scheme.�
In�(a)�above�any adverse impacts is�exophoric�as�the�impacts�referred�to�are�recov-erable�from�the�situation�rather�than�from�the�text.�Any adverse impacts can�be�interpreted�as�‘any�potential�impacts�that�could�arise�in�the�future’.�This�is�similar�to�Halliday�and�Hasan’s�(1976:�71)�example�of�Don’t go, the train’s coming,�which�Halliday�and�Hasan�suggest�interpreting�as�‘the�train�we’re�both�expecting’.�Like-wise,�potential impacts in�example�(b)�can�also�be�paraphrased�in�a�similar�man-ner�to�the�phrase�in�(a),�while�in�(c)�an�examination�of�the�wider�context�reveals�that� the visual impacts of the scheme refers�not� to�specific� impacts�mentioned�previously�in�the�text,�but�rather�to�‘any�potential�impacts�occurring�in�the�future’.�An�analysis�of�the�Means-Purpose�relation�therefore�suggests�that�when�impacts occurs�in�a�phrase�without�any�specific�anaphoric�or�cataphoric�reference�it�has�a�strong�tendency�to�occupy�Rheme�position�in�the�sentence.
When�impacts occurs�in�the�Means-Result�relation�(13�instances),�it�is�found�with�the�same�two-way�signalling�verbs�(e.g.�minimise,�mitigate,�reduce)�as�were�found�in�the�Reason-Result�and�Means-Purpose�relations.�The�grammar�used�to�express�the�Means-Result�relation�is�always�the�verb�in�the�passive�followed�by�‘by’�or�‘through’�+�noun/-ing,�with�impacts always�in�Theme�position�and�acting�as�an�A-Noun:
These noise impacts can be mitigated by noise barriers.
The impacts will be minimised by maximising the use of materials from the site excavation into the reclamation and site formation fill materials.
The�Grounds-Conclusion relation�is�signalled�by�a�variety�of�complex�preposi-tions�and�adverbs.�The�five�tokens�for�In view of… and�the�single�token�for�In con-sideration of… are�all�sentence-initial,�with�impacts�having�anaphoric�reference�to�some�kind�of�environmental�problem�elaborated�on�in�the�previous�text,�as�in�the�example�below:
In view of these potential impacts, the EIA concluded that every opportunity must be taken to minimise potential impacts on Sousa arising from the con-struction works.
However,�the�other�signals�of�the�Grounds-Conclusion relation,�namely�As a re-sult� (4),�Therefore� (2),�Thus (2),�Hence� (1),� and�Consequently (1)� are� invariably�used�to�indicate�the�lack�of�a�major�problem,�where�impacts occurs�with�an�evalu-ative�adjective:
� Chapter�5.� PROFCORP:�Problem�element� 71
Consequently, no significant impacts would result upon the marine environ-ment.
As a result, no adverse environmental impacts are expected.
Non-causal�categories�for�impacts
The�remaining�459�tokens�for�impacts, which�do�not�fall�into�any�of�the�causation�relations�examined�above,�mostly�cover�the�evaluation�of�the�Problem�element.�These�specifically�relate�to�the�monitoring�and�assessment�aspect�of�the�impacts�and�are�usually�accompanied�by�a�specific�noun�modifier�(e.g.�dust,�noise)�as�ex-emplified�below:
Model (FDM) was used to examine potential dust impacts during construc-tion.
Noise impacts were also assessed from the proposed transport terminus.
It�was�noted�that�in�the�causal�categories�impacts�had�a�very�similar�patterning�to�that�of�problems and�for�this�reason�can�be�seen�as acting�as�a�type�of�covert�synonym�for�problems.�However,�in�the�non-causation�categories,�problems�has�a�superordinate�role,�with�impacts�acting�as�a�hyponym,�which�is�reinforced�by�its�premodification�by�adjectives�such�as�noise�and�dust�in�the�above�examples.�These�findings�highlight�the�value�of�the�ACRONYM�project�(Renouf�1996)�referred�to�earlier,�which�has�as�its�objective�the�automatic�retrieval�of�hyponymic�elements�through�collocational�profiling,�i.e.�by�identifying�the�most�significant�collocates�of�the�superordinate�term.
The�following�section�will�examine�the�various�lexico-grammatical�pattern-ings�of�impact and�compare�these�with�impacts to�determine�to�what�extent,�if�any,�different�forms�of�a�lemma�pattern�differently.
Causal�categories�for�impact
There�are�745�tokens�for�impact, which�occurs�as�a�keyword�in�26�out�of�the�60�reports.�Table�5-2�in�the�previous�section�gives�a�breakdown�of�the�tokens�for�im-pact across�the�five�different causation�categories.�Out�of�the�total�of�745�tokens�for�impact�88�of�these�act�as�headings�and�sub-headings�and�5�are�of�a�verbal�form�so�these�have�been�excluded�from�the�following�analysis,�leaving�652�tokens�for�both�the�causal�and�non-causal�categories
Although�there�is�a�difference�between�impacts�and�impact�in�terms�of�the�total�number�of�tokens�and�their�keyness�across�reports,�their�distribution�across�
72� Corpus-based�Analyses�of�Problem-Solution�Pattern�
the�different�causation�categories�is�remarkably�similar.�For�example,�45%�of�the�total�number�of�tokens�for�impact�(excluding�headings�and�verbal�forms)�has�a�causative�function,�which�is�very�close�to�the�percentage�of�the�tokens�for�impacts,�with�48%�having�this�function.�However,�within�the�causal�relations�the�percent-age�distribution�is�also�very�similar.�72%�of�the�tokens�for�impact fall�within�the�Reason-Result category�compared�with�77%�of�the�tokens�for�impacts.�Likewise,�17%�of�the�tokens�for�impact�and�16%�of�the�tokens�for�impacts�fall�within�the�Means-Purpose category.�Moreover,�as�the�following�analysis�will�demonstrate,�there�are�many�similarities�between�the�lexico-grammatical�patterning�of�impact�and�impacts within�these�five�causal�relations.�The�main�difference�between�im-pact� and� impacts� lies� in� the� frequency�distributions�of� the� lexico-grammatical�patternings,�as�explained�below.
First�of�all,�it�was�the�explicit�and�implicit�causative�verbs�occurring�with�im-pact�to�signal�the�cause/reason relation�which�were�most�prominent�in�the�Rea-son-Result category,�as�was�also�found�to�be�the�case�with�impacts.�There�was�a�total�of�98�explicit�causative�verbs�with�the�following�number�of�tokens�for�each�of�the�following�types:�cause (22),�result in�(16),�generate (4),�with�55�tokens�recorded�for�the�phrase�have an impact on�and�one�token�for�the�more�forceful�phrase�exert an impact.�These� tokens� for�explicit�causative�verbs�with� impact make�up�46%�of�the�total�number�of�tokens�in�the�Reason-Result category,�whereas�they�only�comprised�19%�for�this�category�with�impacts.�The�main�reason�for�this�difference�lies�in�the�fact�that�the�semi-formulaic�phrase�‘have�an�/�any�impact�/�impacts’�is�found�55�times�with�the�singular�noun,�but�only�five�times�with�the�plural.�This�phrase�was�checked�in�the�Applied�Science�component�of�the�BNC�where�only�33�instances�of�impacts�were� found,�but�661�examples�of� impact occurring�across�187�texts.�To�simplify�the�checking�procedure�a�download�of�one�example�of�im-pact�from�each�text�was�searched�which�revealed�that�some�variation�of�the�basic�pattern�‘have�an�impact’�occurred�in�50�out�of�the�187�lines,�i.e.�27%.�In�contrast,�only�3�instances�of�the�pattern�with�the�plural�lemma�were�found�out�of�the�total�of�33�tokens,�i.e.�(9�%).�These�results�thus�show�that�different�forms�of�a�lemma�of�a�semi-formulaic�phrase�can�indeed�manifest�quite�different�patterning�in�terms�of�frequency�distribution.
An�examination�of�the�tokens�of� impact in�the�result/effect�relation�of�the�Reason-Result�category�shows�that�exactly�the�same�kind�of�patterning�occurs,�but,�again,�with�quite�different�frequency�distributions.�For�example,�the�explicit�verbs�with�impact (e.g.�‘arise�from’,�‘result�from’)�totalled�19�tokens�(8%)�whereas�these�totalled�80�(21%)�with�impacts�in�the�result/effect�relation.�This�difference�can�be�accounted�for�by�the�fact�that�such�verbs�prefer�the�plural�lemma�in�the�lexico-grammatical�patterning:�The direct impacts resulting from the�GIRPD works will be�….�Furthermore,�there�were�only�23�cases�where�impact�was�found�with�
� Chapter�5.� PROFCORP:�Problem�element� 73
‘from’,�in�the�sense�of�‘arising�from’,�but�89�were�recorded�with�impacts.�These�dif-ferences�in�patterning�thus�indicate�that�it�may�not�be�beneficial�to�lemmatise�a�corpus,�as�was�discussed�in�Chapter�3,�as�it�has�been�shown�that�different�lemmas�can�have�different�behaviours.
However,� no� striking� differences� were� noted� between� impact� and� impacts�with�two-way�signalling�verbs�such�as�‘reduce’,�‘minimise’�and�‘mitigate’.�In�fact,�the�types�and�number�of�tokens�(as�a�percentage�of�the�tokens�for�the�Reason-Re-sult�category)�and�the�lexico-grammatical�patterning�were�remarkably�similar.�
Non-causal�categories�for�impact
There�are�356�tokens�out�of�the�652�for�impact which�are�not�causation-related.�Al-though�the�percentage�of�non-causation�tokens�for�impact and�impacts�is�almost�identical�(55%�compared�with�53%),�like�problem�and�problems,�their�functions�are�quite�different.�Whereas�the�tokens�for�impacts�relate�to�the�monitoring�and�assessment�aspect�of�the�impacts,�the�other�tokens�for�impact are�mostly�found�in�the�Introduction�section�of�the�reports,�focussing�on�the�scope�and�background�of�the�studies.�In�many�instances�impact�is�part�of�the�multi-word�item�Environmen-tal� Impact� Assessment� (and� hence� a� technical� term)� prefacing� the� abbreviated�form�EIA�(see�Chapter�3�for�a�discussion�on�how�abbreviated�forms�are�treated�in�PROFCORP).�It�is�therefore�not�surprising�that�most�of�the�tokens�for�impact are�found�in�the�Introduction�sections�where�abbreviated�forms�are�usually�given�in�full�for�their�first�mention,�as�in�the�example�below:
The Environmental Protection Department commissioned ERM Hong Kong to carry out an Environmental Impact Assessment (EIA) to assess the potential environmental impacts involved.
Conclusion
This�chapter�has�thrown�up�some�interesting�findings�regarding�the�lexico-gram-matical�patterning�of�selected�signals�for�the�Problem�element�analysed�within�a�causal�framework.�The�analysis�has�shown�that�causality�plays�a�much�greater�role�in�shaping�the�lexico-grammatical�patterning�of�key�words�signalling�the�Prob-lem�element�than�one�might�have�initially�supposed.�It�has�been�shown�to�per-meate�the�type�of�discourse�under�investigation�and�therefore�supports�Trimble’s�(1985:�59)�premise�that�‘…�so�many�processes�and�other�activities�are�expressed�by�scientific�and�technical�discourse�that�relates�actual�or�hypothetical�causes�and�results’.�At�a� textual� level� causality�has�been�shown� to�be�a� factor� in�anaphoric�
74� Corpus-based�Analyses�of�Problem-Solution�Pattern�
referencing�and�Theme/Rheme�patterning.�At�a�more�delicate� level�of� the� sen-tence�or�clause,�various�types�of�explicit�and�implicit�causative�verbs�have�been�shown�to�be�of�particular�importance�in�realising�causation,�surprisingly,�more�so�than�connectives.�Besides�these�causative�verbs,�the�verbs�‘be’�and�‘have’�in�certain�semi-formulaic�phrases�and�existential�‘there’�with�a�future�time�marker�have�also�been�shown�to�be�markers�of�causation.�Other�lexis,�such�as�‘from’�and�‘associated�with’,� also� not� normally� viewed� as� indicators� of� causation,� have� been� found� to�signal�causal�relations.�
The� following� chapter� describes� a� similar� analysis� carried� out� for� selected�Evoking�and�Inscribed�signals�for�the�Solution�element�in�PROFCORP,�but�within�a�more�functional�rather�than�notional,�i.e.�conceptual,�framework.�
chapter�6
PROFCORPPhraseological�analysis�of�signals��for�the�Solution�element
The�analysis�of�Inscribed�and�Evoking�signals�for�the�Solution�element,�like�those�for� the�Problem�element� in�PROFCORP,� is�also�based�on�the�sub-categories�of�phraseology,�i.e.�collocation,�colligation�etc.�outlined�in�the�previous�chapter.�In�this�chapter�the�lexico-grammatical�patternings�of�items�for�the�Problem�element�were�analysed�within�a�classificatory�framework�of�causative�notions.�However,�a�slightly�different�superordinate�classificatory�framework�is�proposed�for�some�of�the�analyses�in�this�chapter�due�to�the�differences�in�the�nature�of�the�signals�between�the�Problem�and�Solution�elements.�Whereas�all� the�signals�examined�in�the�previous�chapter�were�nominal,�the�signals�for�the�Solution�element�cover�other�grammatical�categories�including�verbal�and�adjectival�use�(see�Table�4-1).
Those�keyword�Inscribed�and�Evoking�signals�which�are�nominal,�e.g.�rec-ommendations,�solutions,�solution,�and�implementation, will�be�analysed�at�the�highest�level�according�to�two�broad�functional�categories�–�‘Proposing�a�Solu-tion’�and�‘Evaluating�a�Solution’,�which�are�described�in�more�detail�in�the�follow-ing�section.�This�is�not�to�say�that�the�lexico-grammatical�patternings�in�which�these�nominal�signals�occur�are�not�involved�in�any�causal�relations.�They�are,�but�they�are�dealt�with�under�a�broader�system�of�functional�analysis.�
The�other�main�categories�of�signals�which�have�been�selected�for�investiga-tion�are�the�adjectival�and�verbal�ones,�which�cover�the�items�recommended�and�proposed (which�could�potentially�belong�to�either�category).�These�will�be�exam-ined�under�the�same�causal�categories�as�those�in�the�previous�chapter�for�the�rea-son�that�the�two�functional�categories�listed�above�are�superfluous�for�the�starting�point�of�this�analysis�as�the�function�of�‘Proposing�a�Solution’�is�intrinsic�to�the�lexico-grammatical�patterning�of�all�the�verbal�tokens,�and�‘Evaluating�a�Solution’�intrinsic�to�the�lexico-grammatical�patterning�of�the�adjectival�tokens�for�these�two�signals.�These�points�are�explained�in�more�detail�in�a�subsequent�section.�
In�addition� to�examining� the�Solution�elements�at�a�more�discourse-based�level,�both�from�a�notional�and�functional�perspective,�this�analysis�will�also�con-sider�similar�points�raised�previously,�i.e.�whether�different�forms�of�a�lemma�pat-
76� Corpus-based�Analyses�of�Problem-Solution�Pattern�
tern� differently,� whether� the� patterns� are� marked� interpersonally,� and� whether�particular�phrases�can�be�associated�with�a�particular�Theme�/�Rheme�position.�
Classificatory framework: Functional categories for nominal signals
The�two�main� functional�categories� into�which�nominal� items� for� the�Solution�element� fall� are� as� follows.� Typical� phrases� from� PROFCORP� for� solution are�provided�as�examples.
– Proposing�a�solution� � The proposed solution is to use…– Evaluating�a�solution� – Positive�evaluation� � …gives a more cost effective solution…� – Negative�evaluation�� …is unlikely to provide a fully effective solution.
Moreover,�the�positive�and�negative�evaluation�functional�categories�are�not�seen�as�polarities,�but�rather�as�operating�on�a�cline.�For�example,�the�negative�evaluation�above�is�hedged,�i.e.�is unlikely to provide…�rendering�it�less�negative.�Although�it�may�seem�rather�anomalous�to�employ�a�different�classification�framework�at�the�macro-level�of�analysis�of�keyword�nouns�for�the�Problem�and�Solution�ele-ments,�with�a�notional�one�used�for�the�former�and�a�functional�one�for�the�latter�for�the�nominal�signals,�this�can�be�justified�on�the�following�grounds.�According�to�Fillmore�(1968,�cited�in�Wilkins’�1976)�the�logical�relations�existing�between�nouns�and�verbs:
…�comprise�a�set�of�universal,�presumably�innate,�concepts�which�identify�cer-tain�types�of� judgement�human�beings�are�capable�of�making�about�the�events�that�are�going�on�around�them,�judgements�about�such�matters�as�who�did�it,�who�it�happened�to�and�what�got�changed.
The�key�concept�here�is�‘judgement’�and�if�we�take�a�look�at�the�example�phrases�in�which�the�word�problem occurs�in�the�five�categories�of�causal�semantic�rela-tions�outlined�in�Chapter�5�we�can�see�that�they�are�all�judgmental�in�nature�as�they�pertain�to�what�is�or�what�might�be�in�the�future.�The�Inscribed�and�Evok-ing�phraseological�items�for�the�Solution�element,�on�the�other�hand,�fall�under�Wilkins’�category�of�communicative�function�Suasion, specifically�the�following:
4.2.1�Inducement�persuade,�suggest,�advise,�recommend,�advocate,�exhort,�beg,�urge,�incite,�propose�� (p.�46)
In�contrast�to�the�‘judgmental’�aspect�of�the�cause-effect�conceptual�category,�this�functional� category� of� Inducement is� seen� by� Wilkins� as� ‘influential’,� i.e.� as� af-
� Chapter�6.� PROFCORP:�Solution�element� 77
fecting�the�behaviour�of�others.�The�examples�noted�above�for�the�functions�of�proposing�and�evaluating�solutions�clearly�belong�to�this�category�as�the�main�dis-course�purpose�of�all�the�reports�in�PROFCORP�and�STUCORP�is�to�persuade,�i.e.�influence�the�readers�of�a�recommended�course�of�action�to�solve�an�existing�problem�as�in�the�case�of�the�STUCORP�reports�or�a�potential�environmental�one�in�the�case�of�the�PROFCORP�ones.�
I�will�confine�the�following�analysis�to�three�nominal�signals�(recommenda-tions,�solution�and�solutions)�and�two�adjectival�/�verbal�signals�(recommended�and�proposed)�for�the�Inscribed�lexis�for�the�following�reasons.�As�my�aim�is�to�examine�the�same�signals�in�each�corpus�this�would�not�be�possible�for�the�other�items�in�PROFCORP,�listed�in�Table�4-1,�which�only�occur�in�that�corpus.�More-over,�minimise�and�reduce have�already�been�discussed�in�the�previous�chapter�as�they�were�found�to�act�as�two-way�signals�for�the�Problem�element.�
As�for�the�six�Evoking�keyword�items�shown�in�Table�4-2,�implementation,�has�been�chosen�for�analysis�as�it�is�the�only�one�which�also�occurs�in�STUCORP�(but�not�as�a�key�word).�It�is�also�of�a�more�general�nature�than�the�other�Evoking�items�such�as�barriers�and�ordinance�and�for�this�reason�merits�investigation�as�it�is�more�likely�to�throw�up�patterns�which�include�other�Inscribed�and�Evoking�items�showing�how�these�combine�in�the�creation�of�discourse.
Classificatory framework: Grammatical / causal categories for adjectival and verbal groups
As� mentioned� above,� recommended and� proposed,� differ� from� the� other� key-word�signals�in�that�they�are�adjectival�or�verbal�in�nature�rather�than�nominal.�It�has�also�been�noted�in�the�introduction�to�this�chapter�that�it�would�not�be�very�meaningful�to�investigate�this�lexis�under�the�functional�categories�laid�out�in�the�previous�section�as�the�tokens�for�these�signals�automatically�fall�into�the�category�of�‘Proposing�a�Solution’,�where�the�signal�is�verbal�e.g.�Further insulation of noise source is�recommended,�or� ‘Evaluating�a�Solution’,�where� the� signal�has�adjecti-val�status,�e.g.�It is believed that the use of the recommended mitigation measures should reduce impacts….�The�two�different�grammatical�categories�therefore�have�a�default�functional�value.�Where�appropriate,�the�lexico-grammatical�patterning�of�these�signals�will�be�examined�according�to�the�same�causal�categories�laid�out�in�the�previous�chapter.
However�as�the�same�signal�can�be�either�adjectival�or�verbal�it�is�first�of�all�necessary�to�assign�each�of�the�tokens�for�recommended�and�proposed�to�one�of�these�two�categories.�Moreover,�within�the�verbal�category�we�also�have�to�distin-guish�whether�the�signal�is�involved�in�an�impersonal�passive,�subject�accompa-
78� Corpus-based�Analyses�of�Problem-Solution�Pattern�
nied�by�passive,�active�or�clausal�construction,�as�a�particular�structure�may�well�influence�its�notional�orientation�in�the�discourse.�For�this�reason,�I�have�decided�to�commence�the�analysis�of�this�category�of�items�from�a�grammatical�base�at�the�primary�level�of�delicacy,�then�moving�to�a�more�notional�analysis�of�items�under�investigation�in�this�category.�
Analysis of recommendations
Table�6-1�below�presents�a�summary�of�the�number�of�tokens�for�the�Inscribed�signals�in�PROFCORP,�which�tells�us�that�this�Solution�element�of�the�Problem-Solution�pattern�is�realised�by�mainly�adjectival�/�verbal�signals�in�PROFCORP�(i.e.�recommended, proposed).�
There�are�107�tokens�in�total�for�recommendations,�which�occurs�as�a�key-word�in�eight�of�the�reports�in�PROFCORP�(see�Table�4-2).�Twenty-two�of�the�107�tokens�constitute�main�headings,�of�which�10�have�a�dual�function.�Eight�are�part�of�a�heading�labelled�‘Conclusions�and�Recommendations’�and�two�part�of�a�heading�‘Summary�and�Recommendations’,�which�leaves�85�in-text�tokens.
Of�these,�77�tokens�can�be�considered�as�an�aspect�of�‘Proposing�a�Solution’,�although�by�virtue�of�this�lexis�they�are�also�inherently�evaluative�in�nature.�The�analysis�of�the�lexico-grammatical�patternings�of�these�77�tokens�for�recommen-dations in�the� ‘Proposing�a�Solution’�category will�consider�the�choice�of�verbs�collocating�with�this�noun�and�the�grammatical�environment�in�which�these�col-locations�appear,�i.e.�the�lexical�colligations.�Firstly,�43�of�the�77�tokens�occur�with�various�verbs�in�the�active�voice.�This�patterning�has�a�cataphoric�function�of�in-dicating�the�content�of�the�report.�Verbs�such�as�‘present’,�‘highlight’,�‘summarise’�and�‘put�forward’�in�the�present�simple�tense�are�found�in�this�context,�as�in�the�examples�given�below:�
Table 6-1. In-text�tokens�for�Inscribed�signals�in�PROFCORP
Inscribed signals PROFCORPNo. of tokens
Recommended *�400Proposed *�584Recommendations *���85Solutions �������9Solution �����31
*��Occurs�as�a�key�word�in�four�or�more�reports.
� Chapter�6.� PROFCORP:�Solution�element� 79
This executive summary highlights the findings and recommendations of …
This report presents a summary of the main findings and recommendations…
However,�when�verbs�such�as�those�above�occur�in�the�past�tense,�they�have�an�intertextual�function�as�they�make�reference�to�a�previous�study.�
The EIA considered details and … provided recommendations for monitoring�….
In�other�instances�of�intertextuality,�recommendations has�more�the�sense�of�‘re-quirements’�when�it�occurs�with�verbs�such�as�‘address’,�‘meet’�and�fulfil’:
The external design of the CIF should meet the recommendations of….
In�this�context,�recommendations�was�found�to�occur�in�a�Means-Purpose�rela-tion�in�a�few�instances:
A detailed survey has been carried out to fulfil recommendations…
Secondly,�recommendations also�co-occurs�with�verbs�in�the�passive�voice�in�26�out�of� the�77�tokens.�The�most�common�verbs�occurring�in�this�patterning�are�make�(8);�summarise�(5)�and�provide�(5):
The main conclusions and recommendations are summarised as follows:
Recommendations are provided for monitoring and audit requirements:
Recommendations are made to cover the inclusion of necessary infrastructure.
A�closer�examination�of�the�verbs�make,�summarise�and�provide�shows�that�they�apparently�display�different�colligational�patterning.�Both�provide�and�make�are�commonly� followed� by� some� type� of� Purpose� statement,� as� evidenced� by� the�above�examples.�Also,�the�delexical�verb�make�with�recommendations�only�oc-curs�once�in�the�active,�but�eight�times�as�a�finite�passive,�thus�suggesting�a�prefer-ence�for�the�passive�over�the�active.�However,�this�was�not�found�to�be�the�case�with�summarise and�provide,�which�were�used�in�both�active�and�passive�voice.�Another�observation�is�that�the�thematisation�of�recommendations seems�to�be�related�to�its�positioning�in�a�particular�section�of�the�PROFCORP�reports.�Where�recommendations occurs�as�the�Theme�and�is�followed�by�the�passive�voice�in�the�Rheme�part�of�the�sentence�(e.g.�recommendations are summarised as follows…)�it�is�found�in�the�Conclusion�section�of�the�reports.�On�the�other�hand,�where�it�occurs�in�Rheme�position�and�is�preceded�by�the�verb�in�the�active�voice�(e.g.�This report summarises the main findings and recommendations…)�it�is�found�as�part�of�the�Introduction�sections.�
80� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Only�eight�of�the�tokens�for�recommendations occur�in�the�category�of�‘Eval-uating�a�Solution’,�i.e.�in�sentences�which are�overtly�evaluative�in�nature,�signalled�by�adjectives�such�as adequate�and�valid in�the�sentences�below:
Therefore, the recommendations made in Section A should be adequate to miti-gate…
Visual and landscape impacts and mitigation recommendations described in the PDS2 remain valid…
Interestingly,�all� these�eight�evaluative�sentences�display�the�same�lexico-gram-matical� patterning,� with� recommendations forming� part� of� a� nominal� group�containing� a� rankshifted� clause,� e.g.� …recommendations made in Section A...;�…recommendations described in the PDS2…. In� fact,� made occurs� seven� times�in� a� nominal� group,� e.g.� …preliminary recommendations made in this� report…�Therefore,� the�PROFCORP�data� show�that�when� the�delexical�verb� ‘make’�col-locates�with�recommendations,�it�prefers�a�passive�form,�either�as�a�main�clause�verb�proposing�a�solution�or� in�a�rankshifted�reduced�relative�clause�as�part�of�an� evaluation� of� a� solution.� However,� a� cross-comparison� with� the� 246� tokens�of�recommendations�in�the�Applied�Science�component�of�the�BNC�reveals�that�although�‘make’�is�one�of�the�most�frequently�occurring�verbs�with�recommenda-tions,�found�36�times,�its�patterning�is�quite�different�with�26�phrases�found�in�the�active,�and�only�10�in�the�passive.�The�reason�for�this�is�that�the�BNC�data�con-tains�more�interpersonal�markers�as�subject,�e.g.�We made our recommendations about 18 months ago…,�or�the�report�is�personalised,�e.g.�The Copenhagen report … makes recommendations for….�There�were�also�very�few�reduced�relative�claus-es�which�have�an�intertextual�function,�with�only�a�handful�of�examples�noted,�e.g.�…recommendations made in an International Atomic Energy Agency study in 1990.�This�comparison�thus�highlights�the�greater�intertextuality�and�impersonal-ity�of�the�PROFCORP�reports,�contextual�factors�which�are�of�crucial�importance�in�professional�writing�(Bhatia�2004).
Analysis of solutions and solution
Of� the� 16� tokens� for� solutions in� PROFCORP� seven� of� these� act� as� headings�which,� like� recommendations,� have� a� dual� function� e.g.� ‘Environmental� Con-straints�and�Solutions’.�Of�the�other�nine�in-text�tokens�(see�Table�6-1),�two�occur�in�the�Introduction�section�of�the�reports,�indicating�their�objective,�and�can�thus�be�classified�as�‘Proposing�a�Solution’:
� Chapter�6.� PROFCORP:�Solution�element� 81
The central focus was to develop solutions to maximise the development poten-tial…
The�remaining� seven� tokens� for� solutions occur� in� the�body�of� the� report� and�belong�to�the�‘Evaluating�a�Solution’�category�as�they�are�all�preceded�by�an�evalu-ative�adjective�such�as�‘practical’,�‘preferred’,�‘appropriate’,�‘possible’:
Currently housekeeping measures are the only practical solutions to minimise release of contaminants from ….
In�contrast,�the�singular�form,�of�which�there�are�32�tokens�altogether,�patterns�quite�differently.�Firstly,�there�is�only�one�heading�and�this�refers�to�a�specialised�term.�Various�specialised�terms�are�also�found�with�20�out�of�the�31�tokens�for�solution in�the�text�of�the�reports,�e.g.�ammonia solution, dredge solution, engi-neering solution.�The�remaining�11�tokens�belong�to�the� ‘Evaluating�a�Solution’�category,�but�unlike� the� tokens� for�solutions which�carry�a�positive�evaluation,�most�of�these�have�an�aspect�of�negativity�associated�with�them,�e.g.:
Any scheme of pumping leachate water from … is unlikely to provide a fully effective solution.
The AFRF is an interim solution to be operated whilst a permanent supply option is pursued.
Therefore,�solution patterns�quite�differently� from�solutions� in�PROFCORP.�In�order�to�verify�whether�these�observations�are�generalisable,�I�consulted�the�Ap-plied�Science�component�of�the�BNC.�Here,�597�tokens�of�solution were�recorded�in�168�texts,�and�1215�tokens�for�solutions in�200�texts.�An�examination�of�the�downloads�(one�per�text)�revealed�that�in�the�case�of�solution�there�were�some�similarities.�45�out�of�the�200�tokens�for�solution�were�premodified�by�a�specialist�term,�which�were�more�or�less�equally�divided�between�chemical�terms�such�as�copper-sulphate�solution,�formaldehyde solution�and�computer-related�terms�such�as�shrink-wrapped�software solution,�parallel processing solution.�Another�35�tokens�of�solution were�found�to�occur�in�sentences�which�carried�negative�evaluation,�e.g.�…�the client must learn that avoidance is never an appropriate solution�for their anxiety,�with�another�20�tokens�showing�positive�evaluation.�Unlike�the�tokens�for�solutions�in�PROFCORP,�which�always�carried�positive�evaluation,�those�for�solutions�in�the�BNC�were�both�positively�and�negatively�weighted,�with�16�and�14�examples�recorded�for�each,�respectively.�Both�the�PROFCORP�and�the�BNC�data�thus�show�that�there�is�a�tendency�for�solution to�be�more�negatively�oriented�than�solutions.�This�is�quite�a�surprising�finding�as�I�would�not�have�expected�so-lution�to�have�this�negative�semantic�prosody�as�its�inscribed�nature�is�positive.
82� Corpus-based�Analyses�of�Problem-Solution�Pattern�
The�following�section�deals�with�an�analysis�of�the�adjectival�and�verbal�In-scribed�signals�in�PROFCORP.
Analysis of recommended
Table�6-2�presents�the�number�of�tokens�for�recommended and�proposed�in�the�adjectival� and� various� grammatical� categories� that� these� two� similar� types are�found�in.�The�focus�of�the�subsequent�analysis�is�on�the�lexico-grammatical�pat-ternings�of�recommended and�proposed within�these�adjectival�and�various�ver-bal�categories.�The�extent�to�which�these�types�are�involved�in�some�type�of�causal�relation,�either�explicitly�or�implicitly,�will�also�be�examined.�
Below,�I�provide�example�sentences�extracted�from�PROFCORP�to�illustrate�how�recommended is�used�in�the�various�grammatical�categories�presented�in�the�above�table.
Grammatical categories
Example sentences
Premodifying�adjective
Provided�that�the�recommended�mitigation�measures�are�diligently�imple-mented,�it�is�considered�that�construction�activities�will�cause�only�local�and�temporary�disturbance.
Impersonal�passive
It�is�recommended�that�suitable�colouring�and�planting�schemes�be�used.
Subject�+�passive Ambient�dust�monitoring�is recommended�at�the�residential�develop-ments.
Active The�EIA�study�has recommended�that�guidelines�on�good�site�construction�practices�are�included�as�contractual�controls.
Other�clause�construction
Mitigation�measures�recommended�for�the�construction�phrase�will�gener-ally�apply�to�maintenance�dredging.
Table 6-2. In-text�tokens�for�the�adjectival�and�verbal�categories�of�recommended�and�proposed�in�PROFCORP
Grammatical category RecommendedNo. of tokens total
% ofProposedNo. of tokens total
% of
Premodifying�adjective � 95 � 24% 437 � 75%Impersonal�passive 102 � 25% � 36 � � 6%Subject�+�passive 135 � 34% � 57 � � 9.7%Active � 22 � 5% � � 5 � � 1%Other�clause�construction � 46 � 12% � 49 � � 8.3%Total 400 100% 584 100%
� Chapter�6.� PROFCORP:�Solution�element� 83
Out� of� the� 422� tokens� for� recommended,� 22,� i.e.� approximately� 1� in� 20� of� the�tokens,�are�either�headings�or�sub-headings,�which�again�shows�the�significance�of�Inscribed�signals�for�textual�patterning.�A�random�download�of�2000�tokens�of�recommended (the�maximum�number�allowed)�of�the�written�component�of�the�full�BNC�reveals�that�only�25�of�these�function�as�headings�or�sub-headings,�a�ratio�of�1�to�80,�which�underscores�the�significant�use�of�this�type�as�a�heading�in�these�reports.
Of�the�remaining�400�tokens�approximately�25%�are�of�an�adjectival�and�75%�of�a�verbal�form,�as�can�be�gleaned�from�Table�6-2�above.�A�detailed�analysis�of�these�adjectival�and�verbal�categories�is�given�below.�
Recommended�as�premodifying�adjective
The�most�salient�noun�to�collocate�with�recommended is�‘measures’�which�occurs�36� times� in�some�type�of�noun�phrase,�with� the�specific�pattern� ‘recommended�mitigation�measures’�occurring�25�times.�An�examination�of�these�36�noun�phras-es�where�recommended collocates�with�‘measures’�reveals�that�of�these�25�(but�not�the� same�25�as�mentioned�previously)�are� found� in�a�causal� relation.�The�Con-dition-Consequence relation� is� represented� the�most� frequently,�14� times,�with�seven�tokens�for�‘if ’�and�seven�for�‘provided�that�…’�as�in�the�example�below:
Provided that the recommended mitigation measures are diligently imple-mented, it is considered that construction activities ….
There�are�six�examples�of�implicit�causative�verbs,�with�one�in�a�Purpose�clause.�Notably,�all�of�these�are�the�three�occurring�as�keywords�in�PROFCORP,�namely,�minimise,�reduce and�ensure,�which�act�as�two-way�signals,�e.g.:
� � It is believed that the use of the recommended mitigation measures should reduce impacts at nearby ASRs to acceptable levels ….
The�remaining�59�tokens�for�recommended,�when�used�as�a�premodifying�adjec-tive,�collocate�with�a�semantic�set�of�nouns�(e.g.�levels,�criteria,�requirements,�plan,�limits),�which�like�the�phrase�‘recommended�mitigation�measures’�signify�some�kind�of�monitoring,�e.g.�‘recommended�control�levels’,�‘recommended�water�qual-ity�levels’,�‘recommended�monitoring�and�audit�requirements’.�Therefore,�in�this�context� the�95� in-text� tokens� for�recommended�are�shown�to�have�a�very� tight�collocational�patterning�semantically.�
84� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Recommended�in�impersonal�passive
102�of�the�400�tokens�of�recommended�(i.e.�25%)�occur�in�an�impersonal�passive�construction,�with�89�found�in�the�present�simple�form:�It / it is�recommended that …�In�contrast,�of�2000�randomly�downloaded�tokens�for�recommended�from�the�written�component�of�the�BNC,�only�99�were�found�in�this�form,�i.e.�2%,�which�shows�the�significance�of�this�pattern�in�PROFCORP.�The�high�frequency�of�this�pattern�across�reports�from�different�companies�is�a�reflection�of�the�convention-alized�style�of�writing�for�this�specific�type�of�EIA�report.�It�was�noted�in�Chapter�3�that�companies�often�have�their�own�“template”�for�writing�such�reports�and�this�“template”�could�well�specify�not�only�report�divisions�and�sub-divisions,�but�also�signalling�phrases�such�as�It is recommended that …
When�this�grammatical�construction�is�used,�an�analysis�of�its�meaning�with-in�the�wider�context�of�the�data�shows�that�it�enters�into�some�aspect�of�causal-ity,�either�explicitly�or�implicitly.�When�it�occurs�in�some�kind�of�explicit�causal�marker,�this�marker�is�operating�at�a�local�level�of�coherence:
In order to avoid this water reserve area, it is recommended that the high rise development be located to the west of the water works.
Due to the high dust levels in the area, it is recommended that monitoring is undertaken.
However�when�this�phrase�occurs�without�any�accompanying�causal�marker,�an�examination�of�the�wider�discourse�context�reveals�that�in�the�majority�of�cases�it�falls�into�the�Grounds-Conclusion�rather�than�the�more�local�discourse-type�of�Reason-Result.�This�is�because�it�usually�occurs�at�the�end�of�a�sub-section�mak-ing�a�recommendation�based�on�content�in�the�preceding�paragraph.�Of�course,�concordancing�can�only�tell�us�what� linguistic� features�are�present� in�a�corpus.�But� it� may� be� possible� to� verify� this� discourse� feature� through� making� It� case�sensitive�as�sub-sections�would�tend�to�begin�a�new�sentence�or�paragraph.�For�example,�under�a�sub-section�headed�‘LANDSCAPE�AND�VISUAL�IMPACT�AS-SESSMENT’,�the�Problem�is�stated�in�the�first�part�as�follows:
Both the construction and operation of the proposed steel mill has potential to result in some landscape and visual impact. … Only the following moder-ate visual impacts were identified: moderate visual impact on walkers in the Countryside Conservation Area…
This�sub-section�concludes�as�follows,�without�any�explicit�signalling�of�Grounds-Conclusion:
� Chapter�6.� PROFCORP:�Solution�element� 85
It was also recommended that landscaping on the road boundary be used to extend the landscape framework and reduce the visual mass of the develop-ment.�
Crombie�(1985)�notes�that�the�Grounds-Conclusion�relation�is�usually�explicitly�signalled,�but�that�does�not�seem�to�hold�true�for�this�data�in�which�there�are�only�18�cases�where� the�Grounds-Conclusion� relation� is�explicitly� signalled�via� the�following:�therefore�(14),�thus (2),�hence (1),�on this basis�(1):
It is therefore recommended that funding and as much lead time as practicable should be made available prior to the commencement of construction …
In�Downing�and�Locke�(1992:�231)�therefore,�consequently and�hence�are�classified�as�having�‘consequential’�meaning,�and�because of this,�for this reason�and�so�as�having�‘causal’�meaning�(see�Table�6-3�below).�However,�as�therefore in�the�above�context�displays�a�more�causal�meaning,�as� recommendations�are�made�by� the�writer�on�the�basis�of�previous�evidence,�I�would�prefer�to�consider�it�as�belong-ing�to�the�‘causal’�category,�and�not�the�‘consequential’�category,�which�denotes�a�fact-based�causal�connection�contained�within�the�propositional�content�rather�than�a�speaker-based�one.
The� above� example� of� Problem� and� Solution� elements� from� the� same� text�support� Fries’� (2001,� 2007)� research� that� the� information� that� is� placed� in� the�Rhemes�of�the�clauses�of�the�Solution�sections�of�the�texts�‘are�cohesively�tied�to�the�description�of�the�problem�and�thus�address�meanings�that�have�already�been�brought� to�attention�and�made� salient� in� the� text’� (Fries�2007:�1).� In� the�above�extracts�the�Problem�element�is�expressed�as�result in some landscape and visual impact�with�the�Solution�occuring�in�the�Rheme,�matching�the�Problem�through�
Table 6-3. Conjunctive�Themes�(from�Downing�&�Locke�1992)
Meaning Example
Additive Also,�in�addition,�besidesAdversative However,�on�the�other�hand,�yet,�converselyAlternative Alternatively,�either�…�or,�insteadAppositive That�is,�for�instanceCausal Because�of�this,�for�this�reason,�soComparative In�the�same�way,�likewiseConcessive Nevertheless,�anyway,�stillConditional In�that�case,�under�the�circumstancesConsequential Therefore,�consequently,�henceContinuative In�this�respect,�as�far�as�that’s�concernedTemporal First,�then,�next,�presently
86� Corpus-based�Analyses�of�Problem-Solution�Pattern�
the�cohesive�lexis�of�reduce visual mass.�Fries�(ibid.:�18)�notes�that�‘Focus on�this�relation�[i.e.�matching�of�Problem-Solution]�is�achieved�when�the�cohesive�tie�is�presented�as�New�information�in�the�Rheme’,�although�these�ideas�have�already�been� mentioned� previously� in� the� description� of� the� problem.� Fries� concludes�that�such�notions�as�Given�and�New�and�Theme�and�Rheme�need�to�be�examined�in�relation�to�the�rhetorical�purposes�of�the�text�segments�in�which�they�occur.�In�the�PROFCORP�data�the�prevalence�of�the�pattern�It / it is recommended that ...�suggests�that�it�may�well�be�being�used�as�a�device�for�setting�up�this�kind�of�matching�relation.
Usually�the�Purpose�aspect�of�the�Means-Purpose�relation�is�found�in�Theme�position,�but�there�are�12�cases�of�the�pattern�‘it�+�verb�+�recommended�that…’�with�a�Purpose�clause�in�Rheme�position.�Moreover,�these�purpose�clauses�tend�to�be�longer�in�length�incorporating�postmodification�of�a�noun�with�a�prepositional�phrase,�as�in�the�following�example:
It is recommended that suitable colouring and planting schemes be used, in conjunction with screening walls, to minimise the visual/landscape impact of these buildings.�
The�foregoing�analysis� thus�reveals� that� in� this�data� ‘it�+�verb�+�recommended�that�…’�has�a�strong�tendency�to�occur�with�various�causal�markers�and�the�type�of�causal�marker�signals�whether�the�phrase�is�operating�inter-sententially�or�at�a�more�global�level.�Where�there�is�no�explicit�causal�marker,�the�phrase�tends�to�have�a�summative�concluding�function.
Recommended�in�subject�+�passive�construction
The�tokens�for�recommended�which�occur�with�a�passive�verb�and�a�subject�total�135,�which�is�34%�of�the�total�number�of�tokens�for�this�type.�In�common�with�the�impersonal�passive,�the�majority�of�the�tokens�for�recommended,�90�out�of�135,�occur�in�a�phrase�with�a�verb�in�the�present�simple�tense,�with�21�and�22�tokens�with�verbs�in�the�past�simple�and�present�perfect�respectively,�and�only�two�tokens�occurring�with�a�modal�verb�(will, can).�
First,�this�construction�shows�a�slight�colligational�preference�for�plural�nouns;�in�60%�of�cases�(i.e.�82�of�the�135�tokens)�the�subject�is�in�the�plural�form.�Signifi-cantly,�however,�around�70%�of�these�are�made�up�of�either�Inscribed�signals�for�the�Solution�element,�(e.g.�mitigation measures, practices, procedures, proposals),�or�Inscribed�signals�for�the�Evaluation�element�(e.g.�requirements, tests, audits),�several�of�which�occur�as�keywords�in�four�or�more�texts�(see�Table�4-).�Examples�of�these�for�both�the�Solution�and�Evaluation�elements�are�given�below.��
� Chapter�6.� PROFCORP:�Solution�element� 87
Appropriate mitigation measures are recommended for each phase.
Special procedures were recommended for the dredging and disposal of ….
Environmental audits are recommended to check the effectiveness of mitigatory measures and thereby …
The�remaining�40%�of� the� tokens�(i.e.�53�out�of�135)� for�recommended� in� this�construction�are�with�a�noun�in�the�singular.�An�examination�of�these�singular�nouns�reveals�that�they�mostly�fall� into�two�distinct�categories.�Firstly,�one�cat-egory�consists�of�Inscribed�signals�of�both�the�Solution�and�Evaluation�elements,�which�occur�as�key�words�in�four�or�more�reports�(refer�to�Tables�4-1�and�4-2):�
A multi-system gas control scheme is recommended to minimize sub-surface lateral gas migration beyond site boundaries…
An emergency response plan (ERP) is recommended to provide a written proce-dure for dealing with emergency situations such as …
A detailed ecological impact assessment is recommended as part of the afteruse contracts for each site.
Effluent monitoring is recommended for the ammonia solution from the nitrous oxide plant …
The�other�category�comprises�nominalisations,�which�happen�to�be�of�the�gram-matical� metaphor� type,� a� major� feature� of� the� discourses� of� science� (Halliday�1998).
The utilisation of quietened equipment … is recommended to minimise…
Thus, provision of indirect technical remedies … is recommended to ensure…
Regulation of privately delivered construction waste is recommended.
The successful implementation of environmental measures is recommended.
Now,�the�next�question�to�ask�is�when�recommended in�this�subject�+�passive�con-struction�might�be�used�instead�of�recommended�in�impersonal�passive�discussed�in�the�previous�section.�It�appears�that�when�certain�verbs�(e.g.�carry out, make, undertake and�consider)�are�used,�there�is�a�preference�for�the�impersonal�passive,�indicating� that� their�nominal�metaphorical� equivalents,�while�possible,� are�not�commonly�used.
It is recommended that a comprehensive environmental audit is undertaken to confirm that the odour control systems are operating….
It is recommended that a reassessment be made ….
88� Corpus-based�Analyses�of�Problem-Solution�Pattern�
By�virtue�of�its�grammatical�nature,�the�nominalisation�occurs�as�Theme�and�rec-ommended�occurs�in�the�Rheme�part�of�the�sentence.�It�is�interesting�to�note�that�some�type�of�Purpose�clause�is�usually�contained�within�the�Rheme�along�with�this�patterning.�Here,�we�have�an�example�of�what�Hunston�and�Francis�(2000)�term�‘clause�collocation’,�as�the�data�shows�that�recommended�in�this�grammatical�structure�has�a�strong�collocation�with�Purpose clauses.
The following mitigation measures are recommended in order that all construc-tion works for the LAR will comply with …
Other means for noise reduction are also recommended to control noise emission at source…
Purpose clauses� are� also� found� in� the� Theme,� but� only� on� 10� occasions,� and�notably�these�are�very�short�without�any�postmodification�of�the�accompanying�noun,�e.g.:
To reduce congestion, regulation of construction waste is recommended.
To alleviate frequent flooding, drainage channels were recommended in the north…
The�prevalence�of�Purpose clauses�with�subject�+�recommended thus�demon-strates�how�intertwined�the�Problem�and�Solution�elements�are�when�solutions�are�being�put�forward.�
Recommended�in�active�voice
In�contrast�to�the�two�types�of�passive�construction�which�make�up�59%�of�the�to-kens�for�recommended,�the�active�is�found�in�only�22�cases,�i.e.�5%�of�the�tokens,�comprising�two�distinct�categories.�In�the�first�category,�recommended signifies�an�aspect�of�intertextuality�when�it�is�used�in�the�past�tense�in�the�Introduction�of�the�reports.�Here,�reference�is�made�to�a�previous�recommendation�by�a�decision-making�body,�which� in� turn�constitutes� the�basis�of� the�present�environmental�report.�As�socio-linguists�belonging�to�the�school�of�New�Rhetoric�point�out:�‘No�text�is�single,�as�texts�refer�to�one�another,�draw�from�one�another,�create�the�pur-pose�for�one�another’�(Devitt�1991:�336).�
In April 1993 the Land Development Policy Committee recommended that detailed planning and design for the first stage of development should include the first eight container berths ….
� Chapter�6.� PROFCORP:�Solution�element� 89
The�second�sense�in�which�recommended�is�used�is�when�it�occurs�in�the�present�perfect�tense�in�the�Conclusion�section�of�the�report,�summarising�recommenda-tions�contained�in�the�Body�of�the�report.
The EIA report has recommended monitoring and audit of noise throughout the construction.
One� striking� difference� between� these� two� uses� of� recommended in� the� active�voice�and�the�uses�of�this�token�in�the�two�passive�constructions�analysed�in�the�previous�sub-section,� is�that�there�are�no�explicit�or� implicit�causal�relations�in�this�lexico-grammatical�patterning.�This�highlights�the�role�that�tense�and�voice�can�play�in�determining�causality,�as�it�has�been�shown�that�when�recommended occurs�in�a�passive�construction�the�majority�of�the�verbs�are�in�the�present�simple�tense,�assisting�in�the�signalling�of�a�Grounds-Conclusion�relation.�
Recommended�in�other�clause�constructions
In�this�type�of�construction,�recommended occurs�46�times,�accounting�for�12%�of�the�tokens�for�this�type.�Where�the�clause�is�a�relative�one,�in�only�three�cases�does�it�occur�as�a�full�clause.�In�all�other�cases�recommended�stands�for�a�defining�reduced�relative�clause,�always�postmodifying,�as�in�the�following�example:
The Sousa mitigation measures and controls recommended in this Report for the construction stage should be incorporated in the detailed design of the AFRF.
Recommended occurs�12�times�in�the�phrase�‘as�recommended’,�which�is�also�a�type�of�reduced�clause.�It�is�noticeable�that�in�all�cases�‘as�recommended’�is�never�in�sentence�initial�position,�but�always�occurs�after�the�proposition�has�been�in-troduced,�as�in�the�following�example:
Contamination mud in the reclamation area will be dredged using a sealed grab as recommended in EPD Contaminated Spoil Management Study….
The�phrase�‘as�recommended’,�I�would�argue�is�a�signal�of�intertextuality�with�a�sim-ilar�function�to�the�phrase�‘recommendations�made�in…’.�Its�frequency�is�salient,�i.e.�of�rhetorical�importance,�in�these�reports�as�this�phrase�only�occurs�52�times�in�the�whole�written�domain�of�the�BNC.�An�examination�of�these�lines�shows�that�it�overwhelmingly�collocates�with�‘by’,�but�there�are�seven�instances�where�‘as�recom-mended’�is�followed�by�‘in’,�usually�referring�to�some�type�of�report.
The�following�section�will�analyse�the�tokens�for�proposed and�compare�its�functions�in�the�different�grammatical�categories�with�those�for�recommended to�note�the�similarities�and�differences�between�these�two�tokens.
90� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Analysis of proposed
In�the�Collins Bank of English Thesaurus� (1998)� ‘recommend’ and� ‘propose’�are�listed�as�synonyms�of�each�other,�but�my�data�show�that�there�is�little�similarity�in� the� distribution� of� ‘recommended’� and� ‘proposed’� across� the� broad� adjecti-val�and�verbal�categories.�While�the�proportion�of�adjectival�to�various�kinds�of�verbal�types�is�25%�to�75%�for�recommended, this�is�reversed�for�proposed.�The�adjectival�and�various�verbal�categories�in�which�proposed occurs,�i.e.�impersonal�passive,�subject�+�passive,�active,�and�clauses,�are�examined�below�and�compared�with�those�for�recommended.
Proposed�as�premodifying�adjective
Proposed as�a�premodifying�adjective�collocates�with�a�quite�different�set�of�nouns�from� those� collocating� with� recommended.� Two� general� patterns� for� proposed�are�noted.�In�37�cases�proposed�collocates�with�the�superordinate�noun�‘Develop-ment�/�developments’,�which� is�also� found� to�be� the�most� frequently�occurring�noun�with�proposed.�In�the�other�cases,�proposed�collocates�with�nouns�which�denote�a�specific�type�of�construction�or�development�being�proposed�in�these�EIA�reports.�These�more� specific�nouns�occurring�with� proposed� cover�a�wide�range� e.g.� flyovers,� container terminals,� highways,� landfill extension� and� marine parks,�to�name�just�a�few.
It�would�seem�that�in�the�context�of�these�environmental�reports�it�is�the�dif-ferent�semantic�sets�of�nouns�found�to�collocate�with�recommended�and�proposed,�i.e.� their�different�semantic�preferences, which�determine�the�meaning�of� these�two�seemingly�similar�adjectival�forms.�According�to�this�data,�we�can�say�that�recommended�is�used�with�a�more�restricted�set�of�nouns,�to�refer�to�some�kind�of�guidelines�for�monitoring�purposes,�whereas�proposed has�more�the�meaning�of�a�suggestion.�But�a�trawl�through�the�Applied�Science�component�of�the�BNC�revealed�that�this�distinction�was�not�found�between�recommended and�proposed in�this�subcorpus�(both�forms�were�used�for�referring�to�some�kind�of�monitor-ing�methods�or�procedures.)�This�distinction�therefore�seems�to�be�specific�to�the�PROFCORP�data�and�alerts�one�to�the�danger�of�overgeneralising,�and�applying�the�findings�from�small-scale�specialised�corpora�to�a�wider�domain�of�the�same�general�topic�area�(see�Gavioli�2002).
� Chapter�6.� PROFCORP:�Solution�element� 91
Proposed�in�impersonal�passive
36�tokens,�i.e�6%�of�the�tokens�for�proposed,�occur�in�the�impersonal�passive.�In�common�with�recommended,�the�majority�of�tokens,�33�out�of�36,�are�found�in�the�present�simple�tense,�with�two�in�the�present�perfect�and�one�in�the�past�sim-ple�tense.�Proposed is�also�found�to�have�a�very�similar�causal�patterning�to�that�of�recommended�in�this�construction.�Where�it�combines�with�a�causal�marker�in�the�Theme�part�of�the�sentence,�this�is�operating�at�a�local�level�of�coherence,�as�in�the�following:
Due to the increasing demand for land and berthing facilities…, it was proposed as part of the CTB study that the reclamation should be extended.
To ensure timely completion of the works, it is proposed to carry out an environ-mental assessment of the extension project in house.
However�when�proposed occurs�without�any�initial�causal�marker�(22�out�of�36�tokens),�like�recommended�in�the�impersonal�passive�it�has�a�more�global�func-tion�as�it�indicates�a�Grounds-Conclusion�relation.�The�following�sentence�con-cludes�a�sub-section�headed�WASTE�ARISING.�Again,�there�is�no�explicit�textual�theme�such�as�‘therefore’.
It is proposed to leave the marine sediments of Tamar Basin in-situ… in order to leave these contaminated sediments undisturbed.
Proposed�in�passive�+�subject�construction
The�tokens�for�proposed�which�occur�with�a�passive�verb�and�a�subject�total�57,�which�is�almost�10%�of�the�total�number�of�tokens�for�this�type.�The�number�of�to-kens�found�in�the�various�tenses�is�as�follows:�present�simple�(27),�present�perfect�(20),�past�simple�(8),�and�one�token�occurring�with�‘can’�and�one�with�‘may’.�An�examination�of�these�tokens�in�context�reveals�that�the�majority�of�them�in�this�aspect�fall�in�the�last�20%�of�the�report,�which�is�the�concluding�section�where�the�verb�is�found�in�the�present�perfect�tense�(cf.�Gledhill�1995)�e.g.:
Detailed radiological monitoring has been proposed, including monitoring in the vicinity of the …
However,�like�recommended,�60%�of�the�tokens�(34�out�of�57)�occur�with�a�plural�noun�as�subject�in�this�construction.�Although�10�of�these�34�subjects�comprise�an�Inscribed�signal�for�the�Solution�element,�with�eight�examples�of�measures and�two�of�recommendations�noted,�the�remaining�instances�focus�on�Evoking�items�which�do�not�occur�as�keywords.�Examples�of�both�types�are�provided�below.
92� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Fifteen measures were proposed to avoid or mitigate air quality impact….
Several new access roads are proposed for the site.
When�proposed occurs�with�a�subject�in�the�singular,�these�nouns�are�very�similar�to�those�found�with�recommended,� i.e.�they�are�either�Inscribed�signals�for�the�Solution�and�Evaluation�elements,�or�they�are�nominalisations,�as�in�the�examples�below:
An audit system is also proposed for both construction and operation phases…
… the adoption of solar energy has therefore been proposed.
Proposed�in�active�voice
The�five�tokens�for�proposed�which�occur�in�the�active�voice�are�all�found�in�the�Introduction�section�of� the�reports.�Like�those�tokens�for�recommended� in�the�Introduction,�they�have�an�intertextual�function�as�they�refer�to�a�previous�docu-ment�which�forms�the�basis�of�the�present�investigation:
A Project Steering Group (PSG) was convened by Government in 1991 to assist in planning this flyover and proposed three possible alignments which are referred to herein as Options A, B and C.
Proposed�in�other�clause�constructions
Interestingly,�clauses�are�the�only�verbal�category�in�which�proposed�is�used�with�the�same�degree�of�frequency�as�recommended. In�all�aspects,�it�has�the�same�se-mantic�and�syntactic�characteristics�as�recommended.�Except�in�four�cases�out�of�49�instances,�proposed constitutes�a�reduced�relative�clause.�It�also�collocates�with�similar�nouns,�which�are�mostly�Inscribed�signals�for�the�Problem�element,�e.g.�‘measures’,�‘scheme’�and�‘construction’.�Moreover,�‘as�proposed’,�in�common�with�‘as�recommended’,�is�never�found�in�sentence-initial�position.
As� for� Inscribed� items� for� the� Solution� element� in� PROFCORP,� the� above�analysis�suggests�that�the�Solution�element�tends�to�be�realised�through�the�ad-jectival�and�verbal�signals,�recommended and�proposed,�rather�than�through�the�nominal� signals� recommendations,� solutions� and� solution.� Moreover,� the� data�indicate�that�recommendations�and�solutions may�have�a�preference�for�differ-ent�lexico-grammatical�patternings,�with�recommendations�having�an�important�intertextual�function�and�solutions a�largely�evaluative�one.�The�analysis�has�also�suggested�that�recommended�and�proposed�are�not�synonymous�and�are�not�used�
� Chapter�6.� PROFCORP:�Solution�element� 93
interchangeably:�recommended�seems�to�be�preferred�in�the�passive�construction�whereas�proposed usually�occurs�as�a�premodifying�adjective.
Analysis of implementation
There�are�six�evoking�items�in�PROFCORP,�which�occur�as�keywords�in�four�or�more�reports.�Out�of�these�implementation has�been�chosen�for�analysis�because�it�is�the�only�one�of�the�Evoking�items,�which�also�occurs�in�STUCORP�(44�to-kens)�and�also�because�it�is�the�Evoking�item�which�has�the�most�general�mean-ing,�as�mentioned�earlier.
133�tokens�are�recorded�for�implementation,�but�only�three�of�these�are�used�as�sub-headings,�thus�indicating�the�more�superordinate�nature�of�the�Inscribed�signals,�which�are�used�as�sub-headings�more�than�the�Evoking�items�such�as�im-plementation.�This�also�suggests�the�importance�of�the�Inscribed�lexis�as�sub-head-ings�for�creating�textual�coherence.�The�130�in-text�tokens�for�implementation are�equally�divided�between�the�categories�of�‘Proposing�a�Solution’�and�‘Evaluating�a�Solution’,�with�65�tokens�occurring�in�each,�which�are�analysed�below.
Of�the�65�tokens�for�implementation under�‘Proposing�a�Solution’,�approxi-mately�a�third�of�these�(21�tokens)�have�the�status�of�a�noun�modifier�with�the�most�common�noun�collocation�being�‘implementation�programme’,�which�oc-curs�nine�times.�Moreover,�the�majority�of�the�tokens�(43�tokens)�for�implementa-tion are�found�in�the�Introduction�sections�of�the�reports,�either�referring�to�the�objectives�or�the�different�phases�/�stages�of�the�plan�to�be�implemented,�e.g.:
The overall objective of the New Airport Master Plan was defined as a compre-hensive scheme for the planning and implementation of an operationally safe and efficient airport.
Ten�of�the�tokens�of�implementation�concern�a�recommendation�and�are�found�in�the�Concluding�sections,�e.g.:
It is recommended that a modified version of past institutional arrangements be adopted for the implementation of the LAPH developments.
There�are�also�65�tokens�for�implementation which�can�be�classified�as�‘Evaluat-ing� a� Solution’� and� here� much� greater� conformity� is� noted� as� these� tokens� are�involved�in�two�distinct�kinds�of�lexico-grammatical�patterning�which�both�entail�an�aspect�of�causativity.�In�the�first�pattern,�of�which�there�are�44�instances,�imple-mentation�is�preceded�by�a�complex�preposition�(e.g.�‘as�a�result�of ’)�or�a�single�preposition�of�the�pattern:�preposition�+�‘the�implementation�of…’,�signalling�the�
94� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Reason-Result�relation.�Such�prepositions�in�this�patterning�are�as�follows:�with�(23);�after�(6);�through�(4);�prior�to�(2);�associated�with�(2);�on�/�upon�(2);�as�a�result�of�(2);�from�(1);�by�(1);�following�(1).�Although�some�of�these�prepositions�are�clearly�causation-related�such�as�‘as�a�result�of ’,�‘through’�and�‘by’,�the�others�are�not�normally�considered�as�relating�to�causation.�I�have�already�made�a�case�in�the�previous�chapter�for�treating�‘associated�with’�as�a�hedged�causative�device.�In�this�context,�I�would�also�like�to�argue�that�other�prepositions�such�as�‘after’,�‘upon’�and�‘with’,�like�‘as�a�result�of ’,�also�signal�Reason-Result as�they�indicate�the�outcome�of�a�course�of�action,�as�shown�in�the�following�examples:
With the implementation of the preferred mitigation option, the estimated num-ber of dwellings exposed to traffic noise levels was reduced to 120.
Residual noise impacts after implementation of mitigation measures will be within established standards and guidelines.
The�co-occurrence�of�various�prepositions�or�prepositional�phrases�with�gram-matical�metaphor�nouns�such�as�implementation suggests�that�this�type�of�pat-terning� may� be� fairly� typical� of� formal� technical� writing.� And� in� fact� a� search�in�the�Applied�Science�component�of�the�BNC�of�the�string�‘With�the…’,�which�was� the� preposition� occurring� the� most� frequently� in� this� sense,� did� throw� up�instances�of�such�patterning,�e.g.�With the evolution of multicellular organisms…;�with the completion of …; with the construction�of….
One�significant�finding�was�that�in�the�PROFCORP�data�the�pattern�–�prepo-sition�/�prepositional�phrase�+�grammatical�metaphor�noun�–�always�signalled�a�positive�outcome�of� the�proposed�recommendation,� thus�suggesting�that� this�patterning�could�have�a�positive�semantic�prosody.�But�where�explicit�causative�verbs,�e.g.� ‘lead�to’�and� ‘result� in’�were�used,� there�was�negative�evaluation�of�a�rejected�solution.�
In addition, implementation of Option 1 would lead to significant disruption of traffic.
The�other�lexico-grammatical�patterning�of�implementation�is�where�it is�thema-tised�and�followed�by�two-way�signalling�verbs�such�as� ‘reduce’,� ‘minimise’�and�‘ensure’,�which�are�keyword�Inscribed�signals�for�the�Solution�element�(see�Table�4-1).�There�are�20�instances�of�such�patterning�with�examples�given�below.
The implementation of the mitigation measures will ensure that the project is carried out….
The implementation of Kam Tin Bypass will reduce traffic noise levels at resi-dences along Kam Tin Road.
� Chapter�6.� PROFCORP:�Solution�element� 95
This�patterning�for�the�Evoking�item�implementation is�quite�different�from�the�patterning� for� the� Inscribed� signals� in� this� category� of� ‘Evaluating� a� Solution’.�Whereas�the�evaluation�of�a�solution�is�realised�by�evaluative�adjectives�with�the�Inscribed�signals,�here� it� is� realised�by�Reason-Result�markers,�either�preposi-tions� or� causative� verbs,� to� signal� the� successful� /� unsuccessful� outcome� of� an�implemented�course�of�action.
Conclusion
The�analysis�of�the�various�signals�for�the�Solution�element�has�shown�us�that�in�PROFCORP�as�far�as�the�nominal�signals�are�concerned�the�tokens�for�recom-mendations are�mostly�concentrated� in� the�category�of� � ‘Proposing�a�Solution’,�whereas the�majority�of�the�tokens�for solutions are�evaluative�in�nature.�We�have�also�seen�that�with�respect�to�the�‘Evaluating�a�Solution’�category,�solution is�more�negatively-oriented� that� solutions,� e.g.� … is unlikely to provide a fully effective solution.
Turning�to�the�verbal�signals,�there�is�far�more�uniformity�in�the�lexico-gram-matical�patterning�of�recommended�and�proposed�in�the�verbal�categories�than�was�found�with�the�seemingly�synonymous�nominal�signals.�In�the�various�verbal�categories�it�is�the�subject�+�passive�construction�which�is�most�illuminating�as�it�shows�the�important�role�of�nominalisations�as�grammatical�metaphor�nouns,�e.g.�…the adoption of solar energy has therefore been proposed.�One�keyword�Evoking�item,�implementation,�which�also�happens�to�be�a�grammatical�metaphor�noun,�was� found� to� have� negative� or� semantic� prosodies� depending� on� the� causative�verb�employed.�
A�socio-contextual�feature�of�writing�uncovered�by�this�analysis�is�the�signal-ling�of�intertextual�features�by�the�various�lexico-grammatical�patternings�of�the�key�words.�For�example,�…recommended in…,��recommendations made in…�and�A project�steering group proposed…�were�different�ways�in�which�this�was�accom-plished.�
Using�the�classificatory�frameworks�outlined�at�the�beginning�of�this�chapter,�the�same�Inscribed�and�Evoking�items�for�the�Solution�element�occurring�in�STU-CORP�will�be�analysed�in�Chapter�8.�The�following�chapter�presents�an�analysis�of�those�Inscribed�and�Evoking�items�described�in�Chapter�5�for�the�Problem�ele-ment�in�PROFCORP�with�reference�to�the�STUCORP�data.
chapter�7
STUCORPPhraseological�analysis�of�signals��for�the�Problem�element
In�this�chapter�I�analyse�the�lexico-grammatical�patterning�of�key�words�for�the�Problem�element�in�STUCORP�using�the�same�causal�vs.�non-causal�categories�as�those�used�for�classifying�the�PROFCORP�data.�My�main�aim�is�to�see�whether�there�are�any�similarities�or�differences�between�the�patterning�of�these�signals.
With� respect� to� the� last� point,� I� take� it� as� axiomatic� that� STUCORP� is,� in�the� first� place,� primarily� good� data,� which� is� a� somewhat� different� perspective�to�previous�research�on�learner�corpora.�In�the�last�few�years,�much�useful�and�valuable�research�has�been�carried�out�learner�corpora�most�notably�by�Granger�(ed.)�(1998b)�and�Granger�et�al.�(eds)�(2002)�with�the�establishment�of�the�Inter-national�Corpus�of�Learner�English,�ICLE�(see�Pravec�2002�for�a�comprehensive�survey� of� learner� corpora).� Significantly,� most� of� this� research� has� focused� on�various�types�of�error�analysis�in�NNS�student�writing�compared�with�NS�writ-ing�of�argumentative�academic�essays�(see�Granger�1998a;�Milton�2000;�Nessel-hauf�2004a;�Barlow�(2005)�provides�an�in-depth�review�of�the�types�of�errors�in�learner� corpora� and� explanations� of� interlanguage� features� in� learner� writing).�In� this�book,� this�distinction�between�NNS�and�NS�does�not�apply�as� it� is�not�a�question�of�whether�the�writer�is�a�native-speaker�or�not,�although�this�obvi-ously�can�have�a�bearing�on�writing�proficiency,�but� rather�whether� the�writer�is�an�expert�or�apprentice�writer.�For�this�reason,�I�treat�the�Learner�corpus�as�a�corpus�in�its�own�right�and�examine�the�major�findings�from�the�perspective�of�whether�the�students�appear�to�have�mastered�the�language�in�accord�with�vari-ous�contextual�parameters.�Comparisons�are�made�between�the�Learner�and�the�Professional�corpus�but�these�are�not�only�for�the�sake�of�establishing�students’�errors�(although,�of�course�various�types�of�sentence-level�deficiencies�are�impor-tant�considerations),�but�are�more�for�the�purpose�of�ascertaining�to�what�extent�student�writing�is�like�or�unlike�expert�writing,�taking�into�account�the�different�contextual�and�situational�features�of�each�corpus.�
It�is�not�assumed�that�differences�necessarily�indicate�deficiencies�in�student�writing;�nevertheless,�the�STCORP�data�cannot�be�regarded�as�having�the�same�uncomplicated�status�as�the�PROFCORP�data.�Where�differences�do�arise�which�
98� Corpus-based�Analyses�of�Problem-Solution�Pattern�
may�diverge�from�normal�practice�in�English,�I�have�consulted�the�Applied�Sci-ence�component�of�the�BNC�to�establish�whether�this�is�a�specific�feature�of�ap-prentice�writing�or�could�be�considered�as�competent�writing�of�a�different�kind�to�that�found�in�the�expert�corpus.�
In�the�following�section�the�Inscribed�signals�problem, problems and�need,�which�occur�as�key�words�and�also�as�key-key�words�in�STUCORP�(see�Table�4-1),�are�chosen�for�detailed�analysis.�The�focus�of�this�analysis�is�a�comparison�of�these�items�with�their�counterparts�in�PROFCORP�(see�Chapter�5)�to�examine�to�what�extent�student�writing�mirrors�professional�writing.�As�no�Evoking�signals�surfaced�for�the�Problem�element�in�STUCORP,�the�analysis�does�not�deal�with�this�category.
Analysis of problem and problems
Table�7-1�below�presents�a�summary�of�the�in-text�tokens�for�problem�and�prob-lems in�both�STUCORP�and�PROFCORP�based�on�the�classificatory�framework�outlined�in�Chapter�5.�
It�has�already�been�noted�that�there�were�no�examples�of�problem�or�problems�acting�as�(sub)-headings�in�PROFCORP.�However,�there�were�19�tokens�of�prob-lem�(4%�of�total)�and�28�tokens�of�problems (10%�of�total)�used�as�sub-headings�in�STUCORP.�One�reason�for�this�is�probably�that�students�are�assimilating�into�their�own�work�the�sub-headings�used�in�several�exemplar�reports�they�have�been�exposed�to�in�class�teaching�to�familiarise�them�with�the�structure�and�content�of�typical�recommendation�reports.�In�fact,�the�materials�contain�a�few�exercises�on�the�use�of�headings�and�sub-headings�in�which�students�are�asked�to�assign�either�
Table 7-1. In-text�tokens�for�problem�and�problems�in�PROFCORP�and�STUCORP
Corpus PROFCORP STUCORPInscribed signal Problem Problems Problem Problems
(SUB)-HEADING � 0 � 0 � 19 � 28CAUSAL�RELATIONReason-Result 29 20 � 84 � 65Means-Result � 2 � 0 � � 6 � � 4Grounds-Conclusion � 1 � 3 � � 5 � � 0Means-Purpose � 6 10 � 48 � 21Condition-Consequence � 1 � 2 � � 7 � � 1Total�(causal) 39 35 150 � 91Non-causal � 2 16 323 170Overall Total (In-text) 41 51 473 261
� Chapter�7.� STUCORP:�Problem�element� 99
structural�headings�(e.g.� ‘Problems’,� ‘Possible�Solutions’)�or�topical�ones�(e.g.� ‘A�new�computerized�system’).�It�is�therefore�not�surprising�to�find�problem /�prob-lems as�sub-headings�in�a�couple�of�these�reports.�
Causal�categories�for�problem
The�most�striking�observation�about�the�in-text�tokens�for�problem�in�STUCORP�is�that�only�31%�of�them�(150�out�of�473)�can�be�categorised�according�to�the�five�semantic�categories�of�causation,�as�presented�in�Table�7-1.�In�contrast,�as�we�have�seen�in�Chapter�5,�39�out�of�the�41�tokens�of�problem�in�PROFCORP�are�causa-tion-related.�I�will�first�discuss�the�different�kinds�of�lexico-grammatical�pattern-ing�of�problem in�the�Reason-Result�category.�
Out�of�the�84�tokens�of�problem in�STUCORP in�this�category,�20�were�found�to�occur�in�the�same�grammatical�environment�as�the�following�nouns:�cause (10),�causes (7),�reason�(1)�factor�(1)�and,�factors (1).�Surprisingly�there�was�only�one�instance�of�its�occurrence�with�a�complex�preposition,�i.e.,�Such problem may be due to the fact that�….�
There�were�43�tokens�for�problem�which�collocated�with�various�types�of�ex-plicit�verbs�marking�causation,�thus�indicating�negative�semantic�prosody.�First�of�all,�the�following�explicit�causative�verbs�(11)�were�used:�cause�(4),�lead to�(2)�with�one�token�for�each�of�the�following�verbs:�bring,�create,�become,�pose,�and�incur.�As�with�the�data�for�this�type�of�verb�in�PROFCORP,�they�were�mainly�used�in�the�active�voice�with�only�one�example�of�a�passive�(These kind of problem are caused by …)�and�one�example�of�a�reduced�relative�clause�(The general financial problem caused�by …).�In�the�analysis�of�these�verbs�in�PROFCORP�a�case�was�made�for�treating�‘be’�as�a�causative�verb�in�a�few�cases,�but�this�function�of� ‘be’�was�not�found�in�STUCORP�where�one�problem�leading�to�another,�a�progressive�multi-layering�of�the�problem�(one�of�the�variations�of�the�pattern�described�in�Chapter�1)�was�expressed�by�verbal�substitutions�in�the�phrase:�This … problem�(e.g.�This causes a problem…;�This may create a problem.).
Nine�examples�where�students�had�tried�to�use�causative�verbs�denoting�re-sult/effect�were�recorded.�Problem�was�used�with�come from�(e.g.�…and the other problem came from …)�in�two�examples�and�with�rise / arise�in�seven.�However,�this�verb�was�only�used�correctly�in�two�cases,�which�were�of�the�pattern�…�prob-lem … has arisen.�The�main�reason�for�the�incorrect�use�of�this�verb�in�STUCORP�is�that�students�are�confusing�an�explicit�causative�verb�marking�result/effect�with�ones�for�cause/reason,�as�exemplified�below:
100� Corpus-based�Analyses�of�Problem-Solution�Pattern�
�*�(a)� It rises a problem that …
�*�(b)� The problem seems�to be arised out of the fact that …
In�(a)�a�cause/reason�verb�such�as�create should�be�used.�In�(b)�the�passive�voice,�which�signals�cause/reason�is�used,�whereas�the�active�voice�should�be�used�here�with�problem to�signal�result/effect.
Now�I�will�examine�the�23�tokens�for�problem�collocating�with�implicit�caus-ative�verbs�which�can�either�have�a�positive�or�negative�semantic�prosody.�Where-as�in�PROFCORP�all�such�verbs�had�the�meaning�of�‘make�the�problem�better’,�in�STUCORP�these�can�be�divided�into�two�groups:�18�phrases�where�the�verb�(e.g.�solve)�has�a�positive�semantic�prosody,�and�five�phrases�where�the�verb�has�a�nega-tive�semantic�prosody�to�convey�the�meaning�that�the�problem�is�exacerbated�in�some�way.�This�difference�between�the�use�of�implicit�causative�verbs�in�the�two�corpora�can�be�accounted�for�by�the�Situation�in�which�the�Problem�is�positioned.�In�PROFCORP�the�environmental�problems�discussed�in�the�reports�are�mainly�potential�ones�which�could�arise�from�any�planned�construction�work,�whereas�in�STUCORP�the�problems�already�exist,�as�evidenced�by�primary�and�secondary�source�data�in�the�student�reports.
With�regard�to�those�five�implicit�verbs�with�a�negative�semantic�prosody�oc-curring�with�problem,�they�were�either�not�used�correctly�or�only�marginally�so.�In�some�cases�the�student�had�attempted�to�use�an�implicit�verb�as�an�explicit�one,�as�in�the�example�below:
* This situation will deteriorate the problem of …
In�other�cases,�the�student�had�used�the�passive,�e.g.�…the problem will probably be worsened,�but�in�native-like�English�(Pawley�&�Syder�1983)�this�concept�would�more�likely�be�expressed�by�an�explicit�causative�verb�+�noun,�derived�from�the�implicit�verb,�e.g.�…will lead to a�worsening of the problem.�In�fact,�a�cross-com-parison�with�the�BNC�shows�that�out�of�272�instances�of�‘worsened’,�only�14�of�these� were� in� the� passive,� and� always� past� tense,� thus� strongly� suggesting� that�the�passive�use,�although�possible,�is�not�usual.1�Another�reformulation�for�this�ergative�verb�could�be�…the problem will worsen.�As�pointed�out�by�Celce-Murcia�(2002)�such�types�of�ergative�verbs�are�particularly�problematic�for�ESL�writers,�noting�that�overpassivation�is�a�common�type�of�error�made�by�advanced�learners�who�have�yet�to�master�the�middle�voice�(ergative)�in�their�writing.�
As�for�the�18�implicit�verbs�denoting�some�kind�of�solution�to�the�problem,�there�are�11�tokens�for�solve,�two�tokens�for�attend to,�and�one�for�each�of�the�fol-
1. I�used�the�whole�BNC�here�as�the�Applied�Science�component�only�contained�19�instances�of�‘worsened’,�only�four�of�which�formed�part�of�a�passive�construction.
� Chapter�7.� STUCORP:�Problem�element� 101
lowing�verbs:�resolve,�ease,�fix,�reduce,�get rid of.�In�the�sentence�below,�not�only�is�get rid of�an�inappropriate�register�for�the�formal�context�of�recommendation�report�writing,�but�it�is�also�incorrect�semantically.�The�problem�refers�to�the�stu-dents�booking�the�sports�facilities�and�not�turning�up,�so�a�more�appropriate�verb�semantically�in�this�case�would�be�‘resolve’�rather�than�‘eliminate’,�the�more�formal�equivalent�of�‘get�rid�of ’:
In order to get rid of this problem, we had proposed two penalty scheme.
Furthermore,�a�check�with�the�Applied�Science�domain�of�the�BNC�reveals�that�out�of� the�82� instances�of� ‘get*�rid�of ’,�problem�only�occurs�once�and�here� it� is�postmodified� (e.g.� this gets rid of the problem of shrinkage and swelling).� In� all�other�cases,�‘get*�rid�of ’�is�found�with�Evoking�items�(e.g.�…shallow injection is best suited to getting�rid of dirty water...),�thus�suggesting�that�Inscribed�and�Evok-ing�lexis�each�have�their�own�preferences�for�verb�collocations,�as�was�also�noted�for�the�verbs�pose*,�present*�in�Chapter�5.
There�was�also�one�example�where�the�student�had�substituted�a�preposition�for�a�verb,�e.g.�…have a very good policy against the problem.�In�the�whole�BNC�slight�variations�on�the�phrase,�against … problem,�are�found�10�times,�but�always�in�the�context�of�‘encountering�a�problem’,�e.g.�…come up against a problem,�and�never�in�the�sense�of�‘solving�a�problem’.
With�regard�to�the�two-way�signalling�of�both�the�Problem�and�Solution�ele-ments,�what�we�find�with�the�STUCORP�data,�which�we�did�not�find�in�PROF-CORP,�is�that�this�relation�is�very�often�expressed�in�a�lexico-grammatical�phrase�containing�the�two�nouns,� ‘solution(s)’�and� ‘problem’.�In�PROFCORP,�this�dual�signalling�was�always�expressed�by�a�verb,�e.g.�minimise,�reduce�+�the�noun�prob-lem.�However,�out�of�the�20�tokens�of�problem�in�the�STUCORP�data, ten�occur�in�a�phrase�where�a�specific�solution�is�proposed.�Of�these,�eight�tokens�of�problem�are�found�in�the�Rheme�position�of�the�sentence:�
… will be a possible solution to the problem.
… may be a solution to this problem.
…is not a technically feasible solution to the problem.
One�reason�for�the�occurrence�of�this�kind�of�metalanguage�for�the�Problem-So-lution�pattern�in�the�STUCORP�data�could�well�be�that�students�are�over-relying�on�such�metalanguage�because�they�lack�knowledge�of�the�range�of�implicit�verbs��(e.g.�alleviate, eliminate)�found�in�the�PROFCORP�data.�Another�observation�is�that�in�the�PROFCORP�data�modals�such�as�would,�should and�could�are�used�to�convey�the�possible�degree�of�success�of�the�proposed�solution,�e.g.�Daily, or more
102� Corpus-based�Analyses�of�Problem-Solution�Pattern�
frequent covering of deposited waste with inert material should minimise much of the problem.
However,�in�STUCORP�in�this�context,�there�are�no�instances�of�these�modals,�with�students�using�possible on�only�four�occasions to�convey�this�epistemic�use,�e.g.�…will be�a possible solution to the problem.�This�suggests�that�students�either�see� no� need� for� modal� marking,� or� more� likely,� have� a� very� limited� repertoire�of�modal�expressions,�an�observation�which�has�also�been�made�in�a�number�of�other�studies�on�learners’�lack�of�epistemic�devices�to�mitigate�their�claims�(see�Flowerdew�2000;�Hyland�&�Milton�1997;�Lorenz�1998).
In�the�STUCORP�data,�the�other�ten�phrases�containing�‘solution(s)’�+�‘prob-lem’�operate�at�a�metadiscourse�level,�with�‘solution(s)’�acting�cataphorically�and�‘problem’�anaphorically�beyond� the� sentence�boundary,� as� in� the�examples�be-low:
… and to suggest some solutions to this problem.
…we suggest some feasible solutions for the problem.
This�kind�of�explicit� signalling�was�not�present� in� the�PROFCORP�data�which�may�well�be�because� two�key�sub-headings� in� the�reports�(‘Environmental� Im-pacts’�and� ‘Mitigating�Measures’)� fulfilled� the�same�function,�and�therefore�ex-plicit�signalling�in�the�body�of�the�reports�was�considered�redundant.�However,�variations� on� this� lexico-grammatical� patterning� ‘solution(s)….problem’� were�found�in�the�BNC,�but�this�pattern�was�more�common�with�‘solution…�problem’�(323�instances)�compared�with�just�45�instances�of�‘solutions�…�problem’.
A�similar�type�of�explicit�metadiscourse�signalling�was�also�found�in�the�lexi-co-grammatical�phrases�for�the�48�tokens�of�problem�in�the�Means-Purpose�rela-tion�(see�Ädel�2006�for�a�corpus-based�analysis�of�learner�metadiscourse).�In�this�category,�there�are�28�phrases�which�are�a�variation�of�the�pattern�‘solution’�+�(in�order)�‘to�solve’�+�‘problem’.�Several�examples�are�provided�below:
…we will suggest possible solutions to tackle this problem.
…recommendation to solve the problem.
…another method to solve the problem.
Of� the� remaining� 20� tokens� of� problem� in� purpose� clauses,� one� occurs� in� the�grammatical�construction�‘so�as…’�(…so as to solve the present problem),�one�to-ken�is�found�after�‘so’�(…so the problem of … can be solved),�and�18�are�found�in�‘in�order�to’�clauses,�11�of�which�occur�in�Theme�position�in�the�sentence,�and�9�in�Rheme�position,�a�pattern�and�distribution�very�similar�to�those�in�the�PROF-CORP�data.�
� Chapter�7.� STUCORP:�Problem�element� 103
It�has�already�been�noted�that�students�have�difficulty�in�using�causative�verbs�and� collocational� appropriacy� is� also� another� area� which� poses� some� difficulty.�Two�verbs�used�by�students,�cope with�(4)�and�get rid of�(1)�are�a�little�informal�for�the�context�and�it�might�have�been�better�to�have�substituted�‘deal�with’�and�‘re-solve’�respectively.�There�are�four�tokens�for�‘improve’�(e.g.�…recommendations to improve the above problem).�In�these�cases,�it�might�have�been�more�appropriate�to�have�substituted�‘problem’�with�‘situation’�as�what�the�students�are�referring�to�is�an�existing�situation�which�is�problematic,�i.e.�the�lack�of�payphones�on�campus:
The following are some recommendations to improve the above problem: to install more payphones campus…
A�check�with�the�Applied�Science�component�of�the�BNC�did�not�yield�any�in-stances�of� the�collocation� improve�+�problem,� so� the whole�BNC�was�searched.�This�search�revealed�that�‘improve’�does�occur�with�‘problem’,�but�this�is�only�in�three�cases�and�in�all�of�them�it�is�some�kind�of�health�problem�which�is�being�referred�to,�e.g.�His surgeon has said that two years’ rest may improve the problem significantly.
The�lexico-grammatical�phrases�for�both�the�Means-Result�and�Condition-Consequence�relations�tended�to�be�rather�formulaic,�mostly�of�the�pattern�This problem can be solved by …� for�the�former,�and�If there is a problem�…�for�the�latter.�The�phrases�in�the�Grounds-Conclusion�category�(e.g.�So, this problem is still in a�controversial stage)�were�all�evaluative�in�nature,�which�confirms�the�find-ings�of�previous�small-scale�research�of�student�writing�where�causation�was�the�rhetorical�function�under�investigation�(Flowerdew�1998b).
Non-causal�categories�for�problem
I�now�consider�the�role�of�the�remaining�323�tokens�for�problem�which�cannot�be�classified�under�any�of�the�five�semantic�relations�denoting�causation.�21�of�these�tokens�were�found�in�sentences�relating�to�the�aim�of�the�investigation,�e.g.:
In this project our aim is�to investigate seriousness of copyright problem in the Hong Kong University of Science and Technology.
Sentences�such�as�the�one�above�stating�the�objective�of�the�project�were�not�found�in�PROFCORP,�where�a�statement�reflecting�the�objective�of�the�report�was�en-cased�in�a�Means-Purpose�relation�with�problem taking�a�plural�form:
An initial environmental impact assessment was commissioned with a view to identifying any insurmountable environmental problems…
104� Corpus-based�Analyses�of�Problem-Solution�Pattern�
However,�the�remaining�tokens�for�problem�in�STUCORP�were�found�in�the�sec-tion� of� the� reports� on� describing� and� discussing� the� findings,� usually� with� the�verb�‘be’.�In�the�context�of�these�student�reports�when�problem�is�used�with�part�of�the�verb�‘be’,�this�verb�functions�as�a�stative�verb�denoting�the�existence,�or�relat-ing�to�the�evaluation,�of�a�problem,�as�in�the�examples�below:�
A third problem is insufficient ink.
…belongings unattendance is a very serious problem.
In�78�cases�problem was�premodified�by�evaluative�adjectives�such�as�common,�important,�significant,�severe,�serious,�main,�major.�When�premodified�by�certain�of�these�adjectives�(e.g.�common, serious, severe, significant,�important),�it�tended�to�be�anaphoric,�but�a�type�of�anaphora�operating�at�the�sentence�rather�than�at�the�discourse�level,�e.g.�…�the lack�of modem lines is really a serious problem.�In�several�cases,�students�had�used�the�referent�‘It’,�e.g.�It is really a serious problem,�when�‘This’�might�have�been�expected�for�retrospective�reference�(see�Lin�2002�for�a�corpus-based�study�on�the�overuse�and�misuse�of�‘It’�in�the�writing�of�Chi-nese�learners�of�English).
One�striking�use�of�premodification�was�that�of�the�ordinatives�such�as�first,�second,�third,�and�next,�and�the�deictic�another,�with�12�tokens�recorded�for�the�ordinatives�and�16�tokens�recorded�for�another.�Phrases�containing�ordinals�(e.g.�The next / second / third problem …)�and�those�with�main, major�and�minor,�which�usually�combined�with�ordinals�(e.g.�The first major problem�…)�had�cataphoric�reference,� always� sentence-internal,� with� the� main� lexico-grammatical� pattern�being�‘problem’�+�‘be’�+�noun,�and�a�few�cases�of�the�pattern�‘problem’�+�‘be’�+�‘that�clause’,�as�in�the�examples�below:
The second major problem is the power failure problem.
The main problem is that the Division of Humanities could not allocate resources to establish such a centre now.
12�out�of�the�16�tokens�for�another premodifying�problem also�displayed�a�similar�type�of�patterning,�e.g.�Another problem is / was (that) the…. These�findings�are�consonant� with� Schmidt’s� (2000)� corpus-based� research� on� the� types� of� nouns�classified� as� Inscribed� signals� in� this� article,� who� notes� that� ‘The� lexico-gram-matical�use�of�‘Problem’�nouns�is�marked�by�a�distinct�preference�for�the�patterns�N-be-that,�th-N�and�th-be-N.’�(p.�122).�
In�sum,�these�data� therefore� indicate� that�when�students�use�problem�with�premodifying�adjectives,�the�type�of�premodifying�adjective�accompanying�it�de-termines�its�anaphoric�or�cataphoric�status.�Another�point�to�note�is�that�in�the�
� Chapter�7.� STUCORP:�Problem�element� 105
above�examples,�the�anaphoric�and�cataphoric�referencing�is�always,�except�in�a�couple�of�cases,�sentence-internal.
However,� the�most�significant� fact�about�the�anaphoric�and�cataphoric�ref-erencing� patterns� associated� with� problem in� STUCORP is� that� it� is� markedly�different�from�that�found�in�the�previous�sub-section,�which�examined�the�lexi-co-grammatical�patterning�of�problem�when�it�was�involved�in�a�causal�relation.�There,�regardless�of� the�causal�category,�problem�was� invariably�anaphoric,�but�most�importantly,�operating�beyond�the�sentence�boundary�(e.g.�…recommenda-tion to solve the problem).�Likewise,�in�PROFCORP,�the�same�kind�of�anaphoric�referencing� at� the� discourse� level� was� present� in� the� causation-related� phrases�(e.g.�…should minimise�much�of the problem).�[An�exception�to�this�was�when�the�indefinite�article�was�used�to�refer�to�a�potential�rather�than�an�existing�problem:�e.g.�…the effluent�export scheme will create a noise problem].�Interestingly,�Scott�(2001b)� found� that�problem had�a�more� local� scope� than�discourse-organising�function�in�a�corpus�of�Guardian�newspaper�feature�articles,�which�seems�to�be�the�case�when�it�is�not�involved�in�a�causation-based�relation.�The�functions�of�problem�either�operating�as�a�local�discourse�signal�or�as�a�more�global�connec-tive�one,�i.e.�as�an�A-Noun�binding�adjacent�clauses�or�sentences,�or�as�an�evalua-tive�or�as�an�evaluative�one�supporting�the�anaphoric�status�of�the�determiner�This�in�the�phrase�This problem has�already�been�brought�up�in�Chapter�1�in�the�review�of�Vocabulary�3�items.�Possible�explanations�for�the�differences�in�these�two�roles�of�problem are�discussed�in�more�detail�below.
According�to�Francis�(1986,�1994),�problem�is�one�of�the�most�common�dis-course-organising�anaphoric�nouns,�which�she�terms�‘A-Nouns’�(Schmidt�refers�to�such�discourse�organising�nouns�as�‘shell�nouns’).�Now,�if�we�examine�the�ex-ample�provided�in�Francis’�1994�article�(p.�85),�we�find�that�it�is�premodified�by�‘this’,� and� also� happens� to� be� involved� in� a� causal� relation,� signalled� by� ‘to� get�around’:
…the� patients’� immune� system� recognised� the� mouse� antibodies� and� rejected�them.�This�meant�they�did�not�remain�in�the�system�long�enough�to�be�fully�ef-fective.The� second� generation� antibody� now� under� development� is� an� attempt� to� get�around� this problem� by� ‘humanising’� the� mouse� antibodies,� using� a� technique�developed�by�…�� (Francis�1994:�85)
Francis’�assumption�is�generally�shown�to�be�valid�where�problem in�both�STU-CORP� and� PROFCORP� is� involved� in� some� kind� of� causal� relation.� However,�other� data� in� STUCORP� (i.e.� that� relating� to� non-causation� phrases)� does� not�support�Francis’�premise.�Moreover,�Hoey�(1998)�also�remarks�that�Francis�seems�to� be� overstating� the� anaphoric� importance� of� these� signalling� nouns� such� as�
106� Corpus-based�Analyses�of�Problem-Solution�Pattern�
problem.�He�points�out�that�nominal�groups�containing�another also�label�a�pre-vious�stretch�of�text,�in�this�case�as�a�problem,�since�another problem�is�given�and�requires�the�reader�to�relate�both�an�earlier�and�later�lexicalisation�of�problem to�fully� interpret� it,� thus�suggesting�that� it� functions�both�cataphorically�and�ana-phorically.�
Also,�as�has�been�noted�in�Chapter�1,�the�anaphoric�function�of�such�nouns�as�problem�is�called�into�question�when�it�co-occurs�with�a�demonstrative�such�as�This,�which�carries� the�anaphora�rather� than� the�noun�problem.�Consulting�the�Applied�Science�component�of�the�BNC�helps�to�shed�light�on�this�issue.�An�examination�of�the�222�concordance�lines�of�This /�this problem�supports�Francis’�notion�of�problem�acting�as�an�anaphoric�noun�at� the�discourse� level.�But� this�anaphoric�use�can�be�explained�by�the�fact�that�138�(i.e.�62%)�of�these�instances�are�causation-based,�with�131�combining�with�a�verb�signalling�the�Solution�ele-ment,�e.g.�This problem was overcome by providing the lamps with locks.�
However,�a�totally�different�picture�emerges�if�we�examine�the�concordance�lines�for�problem with�other�premodifiers�in�the�same�component�of�the�BNC.�For�example,�of�the�36�instances�of�another problem,�only�five�of�these�are�ana-phoric,�where�the�anaphoric�reference�is�always�sentence-internal.�The�majority�have�cataphoric�reference�and�are�of�the�same�patterning�as�those�found�in�STU-CORP.�Moreover,�only�five�of�these�are�causation-based,�all�employing�the�verb�‘arise’.� Not� surprisingly,� ordinatives� also� displayed� similar� patterning� to� that� of�another.� As� for� evaluative� adjectives� (e.g.� serious,� common)� examples� from� the�same�component�of�the�BNC�mentioned�previously,�show�the�lexico-grammatical�patterning�to�be�very�similar�to�that�found�in�STUCORP,�i.e.�having�anaphoric�reference�within�the�sentence,�e.g.�…�stray radiations become a serious problem….�However,�in�8�out�of�the�20�concordance�lines�of�serious problem,�the�anaphoric�sentence�referent�is�this,�e.g.�This is a serious problem…,�which�depends�on�a�pre-vious�stretch�of�discourse�for�its�relexicalisation.�What�is�most�significant,�though,�is�that�out�of�the�21�examples�of�serious problem�and�20�examples�of�common prob-lem�examined�in�the�BNC,�there�is�only�one�instance�of�a�causation-related�sen-tence.�Therefore,�unlike�the�data�for�problem�in�causation-related�phrases,�these�data�do�not�support�Francis’�premise�that�problem�is�a�common�A-Noun.�
We�can�therefore�conclude�from�an�analysis�of�the�above�data�in�PROFCORP�and� STUCORP� and� further� examples� from� the� BNC� that� Francis� is� correct� in�saying�that�problem functions�anaphorically�at�the�discourse�level,�but that this statement is�mainly applicable to its role in causal relations.�Moreover,� the�BNC�data�have�also�confirmed�that�problem when�immediately�premodified�by�this or�the plays�an�important�role�in�causal�relations,�which�was�not�found�to�be�the�case�with�other�premodifiers,�such�as�evaluative�adjectives�and�ordinatives.�Another�important�aspect�to�note�is�that�problem�loses�its�status�as�an�A-Noun�when�pre-
� Chapter�7.� STUCORP:�Problem�element� 107
modified�by�This�as�the�anaphor�is�carried�by�the�determiner.�As�Schmidt�(2000:�8)�points�out�‘shell�nouns�and�shell-noun�phrases�can�only�be�studied�appropriately�if�what�they�link�up�with�is�taken�into�account’.�However,�these�data�from�PROF-CORP�and�STUCORP�and�comparative�data�drawn�from�the�BNC�highlight�the�importance�of�also�taking�into�account�the�semantic�relations�that�a�noun�such�as�problem�may�be�involved�in�when�determining�its�discourse-organising�role�(see�Flowerdew�2003�for�a�discussion�of�these�points).
The�tokens�for�problems are�analysed�according�to�the�same�causal�and�non-causal�categories�as�those�above.
Causal�categories�for�problems
35%�of�the�tokens�for�problems�(91�out�of�261)�are�causation-related,�which�is�a�similar�proportion�to�those�tokens�for�problem in�PROFCORP.�Interestingly,�in�the�Reason-Result�category�explicit�causative�verbs�for�cause/reason�were�rarely�used,�the�only�example�being�‘cause’�occurring�three�times�with�problem.�Like-wise,�there�were�relatively�few�occurrences�of�causation�nouns�(7)�and�complex�prepositions� (2).� However,� there� were� 11� occurrences� of� some� kind� of� explicit�causative�verb�for�result/effect,�but�in�only�one�instance�was�the�verb�used�correct-ly.�In�some�cases�there�were�syntactic�errors�in�formation�of�relative�clauses,�which�has�been�identified�as�a�feature�of�Hong�Kong�English�(see�Gisborne�2000).
� � *�There were some problems resulted from low attendance…
* There are some problems and mainly come from …
In�other�cases,�though,�it�was�the�students’�lack�of�vocabulary�which�was�found�to�be�wanting.�For�example,�in�the�sentence�below�dealing with�appears�to�mean�arising from.
� � * … the problems dealing with computer barns…
There�were�four�sentences�where�‘happen’�or�‘appear’ were�used�in�a�rather�unidi-omatic�way,�as�in�the�two�examples�below.�These�verbs�would�be�better�replaced�by�‘occur’�or�‘arise’,�which�were�found�to�collocate�with�problems�in�PROFCORP.
* Generalised problems mostly happen in computer barns.
* More and more problems appear.
As�for�the�two-way�signalling�lexico-grammatical�phrases�conveying�the�resolu-tion�of�a�problem�(which�as�I�have�argued�is�also�a�type�of�causation)�these�are�also�very�similar�to�those�for�problem� in�STUCORP,�which�fall� into�two�categories:�
108� Corpus-based�Analyses�of�Problem-Solution�Pattern�
implicit�verb�+�problems,�or�noun�+�problems.�The�implicit�verbs�collocating�with�problems were�very� similar� to� those�used�with�problem,�with� ‘solve’�occurring�with�17�of�the�25�tokens�for�problems.�In�the�15�cases�where�problems occurred�with�‘solution(s)’�or�a�synonym,�it�always�had�anaphoric�reference�as�did�problem in�this�kind�of�construction,�e.g.�…possible�solutions to these problems.�As�the�pat-ternings� for�problems in� the�Means-Purpose and�Means-Result categories�are�almost� identical� to� those� for� problem� in� these� two�categories,� they�will�not�be�dwelt�on�further�here.�
Non-causal�categories�for�problems
I�will�now�examine�the�remaining�170�tokens�of�problems, which�do�not�belong�to�any�of�the�causation�categories.�In�common�with�those�non�causation-related�tokens�for�problem,�these�either�relate�to�the�purpose�of�the�investigation�which�is�covered�in�the�Introduction�/�Background�section�of�the�reports,�or�the�reporting�of�the�findings�contained�in�the�Body.�However,�one�salient�difference�between�the�non-causation�uses�of�problems�and�problem�in�STUCORP�is�that�the�distri-bution�between�the�tokens�for�problems in�these�two�broad�areas�is�quite�different.�Whereas�only�21�out�of�the�323�tokens�for�problem�(i.e.�6.5%)�relate�to�the�pur-pose�of�the�investigation,�77�out�of�the�170�tokens�for�problems�(i.e.�45%)�do�so.�A�look�at�the�lexico-grammatical�phrases�for�these�tokens�reveals�that�statements�indicating�the�purpose�of�an�investigation�favour�problems�over�problem�e.g.�We would like to know what are the problems on computer usage they are facing.�In�a�few�cases,�problems�had�a�retrospective�function�as�it�occurred�in�the�Conclusion�restating�the�purpose,�e.g.�In this report we have analysed some existing problems in….�The�use�of�problems�in�the�report�Introductions�sets�up�the�following�dis-course�in�the�Body�and no�doubt�explains�why�10%�of�the�total�number�of�tokens�for�problems (28�out�of�289)�were�used�as�a�heading�/�sub�heading�in�the�Body,�whereas�only�4%�of�the�tokens�for�problem had�this�function.�
The�remaining�93� tokens� for�problems� in�STUCORP�which�were� found� in�statements�reporting�the�findings�also�displayed�differences�from�those�for�prob-lem�with�this�function.�First�of�all,�nearly�65%�of�the�tokens�for�problem�had�this�function,�whereas�only�35%�of�the�tokens�for�problems did.�The�main�reasons�for�this�are�twofold�and�can�be�gleaned�from�the�pre-modification�patterns�of�prob-lem�and�problems.�The�tokens�for�problems�often�occur�in�a�kind�of�topic�sen-tence,�with�pre-modification�by�adjectives�such�as�several,�some�and�main,�e.g.:
The result shows that there are several problems in ….
We have discovered that the five main problems of services…
� Chapter�7.� STUCORP:�Problem�element� 109
However,�in�the�analysis�of�the�lexico-grammatical�patterning�for�problem it�was�noted�that�these�tokens�were�premodified�by�enumerator�adjectives,�e.g.�The next problem…;�A third problem…,�which�is�an�elaboration�of�the�topic�sentence,�sug-gesting�that�one�topic�sentence�with�problems�could�generate� two�or� three� fol-lowing�phrases�with�problem.�Although�this�type�of�patterning�did�not�occur�in�PROFCORP�it�cannot�be�regarded�as�a�feature�of�student�writing�only,�as�the�same�kind� of� patterning� occurs� with� problems and� problem� in� the� Applied� Science�component�of�the�BNC,�as�mentioned�previously.�However,�it�could�well�be�that�students�are�overusing�these�topic�sentences�incorporating�sequence�markers.�In�this�respect,�Hinkel�(2002)�found�that�sequence�markers�such�as�first, second�etc.�were�substantially�overused�by�Asian�non-native�speakers.�
To�conclude,�it�can�be�seen�that�the�distribution�of�problem and�problems�in�STUCORP�across�the�five�causal�categories�is�fairly�similar�to�that�of�problem�and�problems in�PROFCORP,�with�most�of�the�tokens�concentrated�in�the�Reason-Result,� and� secondly� the� Means-Purpose� relation.� However,� this� analysis� has�revealed� differences� in� lexico-grammatical� patterning� between� different� forms�of�the�same�lemma�in�both�PROFCORP�and�STUCORP,�thus�lending�weight�to�Hoey’s�(1997,�2005)�argument�that�different�forms�of�a�lemma�pattern�differently.�For�example,�it�was�noted�in�PROFCORP�that�problems and�problem�had�differ-ent�premodification�behaviour�with�problems�premodified�across�all�causal�cat-egories.�Differences�have�also�been�noted� in� the� lexico-grammatical�patterning�of�the�same�lemma�across�the�two�corpora,�such�as�the�absence�of�interpersonal�markers�in�the�form�of�modal�verbs�with�the�tokens�for�problem in�STUCORP.�In�PROFCORP�such�verbs�were�used�as�a�mitigating�device�for�making�recom-mendations,� but� they� were� never� found� in� STUCORP� where� they� would� have�been�appropriate�in�certain�contexts,�thus�confirming�the�findings�of�several�other�corpus-based�studies�which�note�that�student�writing�tends�to�be�too�direct�and�unhedged.�Another�difference�in�the�patterning�of�problem was�where�it�was�used�with� a� two-way� signalling� verb� (e.g.� ‘minimise’,� ‘alleviate’)� in� PROFCORP.� The�verb�‘solve’�was�overwhelmingly�used�by�students,�thus�suggesting�their�limited�vocabulary�range.�However,�as�already�mentioned,�differences�between�the�lex-ico-grammatical� patterning� in� STUCORP� and� PROFCORP� do� not� necessarily�indicate�deficiencies,�and�this�was�found�to�be�the�case�with�topic-like�sentences,�e.g.�We have discovered that the five main problems of services…,�and�sub-topic�sentences,�e.g.�The second major problem is the power failure problem,�which�did�not�occur�in�PROFCORP,�but�were�found�in�the�Applied�Science�Component�of�the�BNC.�It�is�these�types�of�sentences�which�account�for�the�difference�is�the�dis-tribution�of�problem�and�problems between�the�causal�and�non-causal�categories�in�PROFCORP�and�STUCORP.�
110� Corpus-based�Analyses�of�Problem-Solution�Pattern�
In�the�following�section,�I�will�analyse�the�lexico-grammatical�patterning�for�the�noun�need,�which�like�problem�and�problems also�explicitly�signals�a�nega-tive�evaluation�of�a�situation�and�was�found�as�a�key-key�word�in�STUCORP�(see�Table�4-1).�I�will�focus�on�the�nominal�form�only�so�that�the�analysis�is�compat-ible�with�the�analyses�for�problem and�problems�and�can�be�carried�out�under�the�same�analytical�framework.�
Analysis of need
Causal�categories�for�need
In�STUCORP,�out�of�a�total�of�354�tokens�for�need,�228�are�verbal,�120�are�of�a�nominal� form�and�three�act�as�headings.�28� tokens�(23%)�out�of� the�120�noun�forms�for�need were�causation-related,�and�in�some�respects�similar�to�those�in�PROFCORP,�but�with�more�emphasis�on�the�Solution�element.�Verbs�such�as�‘ful-fil’,�‘meet’�and�‘satisfy’�were�found�to�occur�with�need�in�13�phrases,�nine�of�which�belonged�to�the�Means-Purpose category,�as�shown�in�the�examples�below.�
The opening hours of CCST computer barns should be extended in order to meet the need of students.
Its opening hours, 2pm to 2am, can satisfy the need of students.
However,�the�above�differ�from�the�use�of�need +�‘to’�or�‘for’ in�PROFCORP�as�the�combination�of�need�+�‘of ’�in�STUCORP�is�closer�to�the�verbal�use�in�that�it�relates�to�“students’�need”,�i.e.�what�students�need.�
As�previously�pointed�out,�such�phrases�act�as�two-way�signals,�with�the�verb�signalling�the�Solution�element�and�need�the�Problem�element.�This�dual�signal-ling�necessarily�entails�causativity,�although�there�is�no�explicit�marker�as�such.�
Interestingly,� 10� of� the� causation� sentences� in� which� need occurs� contains�an�adverbial�marker:�Therefore (6),�Thus� (2),�So (1),�Hence (1),�but�only�one�of�these�is�incorporated�within�the�sentence,�albeit�with�faulty�sentence�structure�e.g.�…influences the waiting time, thus usually there is no need to wait for a seat.�All�the�other�adverbs,�however,�are�sentence�initial,�as�in�the�examples�below:
Thus, there is no need to buy a new server.
Therefore, there is a need to provide more payphones.
Usually,�these�adverbials�are�regarded�in�EFL�textbooks�as�markers�of�local�coher-ence,�connecting�two�sentences,�but�in�these�examples,�they�are�functioning�at�a�more�global�level,�as�a�summary�conclusion�for�a�previous�stretch�of�text.�For�this�
� Chapter�7.� STUCORP:�Problem�element� 111
reason� they� can� be� classified� as� Grounds-Conclusion� rather� than� Reason-Re-sult;�in�this�respect�the�students�show�writing�maturity.�However,�in�the�phrases�signalling� Grounds-Conclusion in� PROFCORP,� the� adverbial� was� always� in-tersentential�and�never�sentence-initial�as� in� the�STUCORP�examples,�so� from�this�perspective�the�student�writing�can�be�considered�as�lacking�variation�in�the�positioning�of�adverbs.�
Non-causal�categories�for�need
Another�20�tokens�for�need are�of�the�pattern�There is/was …a need to/for,�with�10�tokens�colligating�with�to�+�verb,�e.g.�There is a need to learn Putonghua.�This�type�of�colligation�can�be�viewed�as�what�Benson�et�al.�(1986)�refer�to�as�gram-matical�collocation,�i.e.�the�fact�that�the�grammatical�structure�following�need is�‘to’�+�verb,�‘for’�+�noun,�or�‘of ’�+�noun�in�the�case�of�the�student�reports.�However,�we�also�have�another�type�of�colligation�operating�with�need, which�is�the�one�de-fined�by�Hoey�as�‘the�grammatical�company�a�word�keeps’.�This�refers�not�to�what�the� grammatical� structure� is,� but� rather� to� the� preferred� grammatical� pattern-ing,�as�it�were�(see�Chapter�1�for�a�discussion�of�colligation).�For�example,�in�the�case�of�need,�one�common�patterning�is�with�existential�‘there’.�Also,�need�prefers�the�pattern�There is no need to…, which�is�manifest�in�both�the�PROFCORP�and�STUCORP�data.�There�are�no�examples�of�the�alternative�negative�form�There isn’t�any need to…�in�either�PROFCORP�or�STUCORP.�Likewise,�none�were� found�for� the�near-synonym�problem. To�substantiate� this�point,� I�checked�these� two�patternings�(There isn’t any…�and�There is no…)�with�need�and�problem�in�the�Applied�Science�component�of�the�BNC.�Any only�occurred�with�need� in�three�cases�always�with�an�intervening�adverb,�e.g.�There is seldom any need for…;�There is no longer�any need for…�and�never�with�a�verb�in�the�negative.�The�preferred�colligational�patterning�with�problem�was�always�There is no…,�e.g.�There is no problem with comparators.
Data�from�STUCORP�therefore�show�that�students�are�aware�of�both�the�col-locational�and�colligational�patterning�of�need.�Nevertheless,�one�grammatical�in-felicity�shown�below�which�is�specific�to�the�writing�of�Chinese�learners�of�English�is�the�confusion�of�anticipatory�‘It’�with�existential�‘There’;�Lin�(2002)�shows�that�this�misunderstanding�can�be�attributed�to�the�influence�of�the�students’�mother�tongue.
* It is no need to explain.
Another�key�interlanguage�feature,�that�of�topicalisation�(e.g.�For the problem, it can be solved by…,�has�been�the�subject�of�numerous�studies�(Green�1996)�and�
112� Corpus-based�Analyses�of�Problem-Solution�Pattern�
also� examined� from� a� corpus-based� perspective� (cf.� Green� et� al.� 2000;� Milton�2000).�However,�surprisingly,�I�did�not�find�any�evidence�of�this�L1�transfer�in�my�corpus� data� when� examining� the� lexico-grammatical� patterning� of� problem(s)�and�solution(s).�One�reason�for�this�may�be�that�we�have�addressed�this�point�on�a�recurrent�basis�in�our�teaching�materials�so�it�is�heartening�to�think�that�students�might�have�improved�in�this�area.
The�remaining�tokens�for�need either�centre�around�the�purpose�for�the�inves-tigation�in�the�Introduction�or�the�identification�of�some�type�of�need�in�the�Body�of�the�reports,�which�is�very�similar�to�the�functions�of�the�lexico-grammatical�phrases�for�problems�in�STUCORP�and�those�for�need�in�PROFCORP.�However,�the�orientation�of�the�problem�statements�is�quite�different�in�the�two�corpora.�In�PROFCORP�the�problem�is�taken�as�‘given’,�i.e.�already�established,�and�it�has�been�pointed�out�in�the�previous�section�that�that�the�preferred�patterning�for�this�is,�for�example,�The EIA has identified the need for a flyover.�
On�the�other�hand,�in�the�student�reports�the�Problem�statement�is�not�taken�as� ‘given’�as�part�of� the�writing� task� is� to�provide�evidence� for� the�existence�of�some�kind�of�problem.�As�pointed�out�in�Chapter�3,�student�topics�revolve�around�university�concerns�such�as�a�shortage�of�computers�or�the�lack�of�modem�lines�for�dialling�in.�This�lack,�or�shortage,�was�very�often�conveyed�by�the�word�insuf-ficient which�surfaced�as�a�key�word�in�five�texts�(see�Table�4-1)�and�occurred�114�times�in�STUCORP,�as�in�the�examples�below:
Students think their laser-print quota is insufficient.
…number of available computers in barns becomes really insufficient.
Having�established� the�existence�of�a�problem,� the� students� then�proceeded� to�comment�on�this�in�relation�to�students’�needs,�which�is�why�patterning�along�the�lines�of�‘cannot’�+�meet/fulfil/satisfy�+�need of students�was�common.�
…the number of machines cannot meet the need of students.
…this still cannot satisfy with the need of the students.
Although�some�lexico-grammatical�patternings�for�need are�similar�in�both�cor-pora�(e.g.�There is a need …for/to),�in�other�instances�the�patterning�is�quite�dif-ferent�depending�on�whether�the�problem�statement�is�being�presented�as�already�established�or�new.�These�examples�thus�show�the�importance�of�contextual�pa-rameters�in�determining�and�interpreting�collocational�features,�which�was�em-phasised�in�Chapter�3.
� Chapter�7.� STUCORP:�Problem�element� 113
Conclusion
As�for�the�STUCORP�data,�deficiencies�have�been�found�in�certain�respects.�Stu-dent�writing�displays�a�very�limited�range�of�explicit�and�implicit�verbs�used�for�marking�the�cause/reason�relation,�and�interpersonal�markers�did�not�occur�with�these�verbs�when�they�could�have�been�used�in�some�cases,�thus�supporting�other�corpus-based� studies� which� conclude� that� student� writing� is� too� direct.� More-over,�it�was�found�that�in�the�result/effect�relation,�there�were�many�grammati-cal�infelicities�with�students�confusing�these�verbs�with�those�for�cause/reason.�Students�were�also�unaware�of�alternative�semi-formulaic�phrases,�e.g.�‘this�…�be�a�problem’,�with�students�also�attempting�to�“create”�their�own�lexical�phrases�to�express�an�idea,�which�although�grammatically�correct�sounded�non-native�like.�However,� some�aspects�of�student�writing�were� found�to�resemble�professional�writing.�The�lexico-grammatical�patterning�of�sentences�such�as�The second major problem is …,�while�not�appearing�in�PROFCORP,�were�found�in�the�BNC�Ap-plied�Science�component.�
The�type�of�analysis�which�is�the�focus�of�this�chapter�thus�demonstrates�the�value�of�corpus-based�work�for�identifying�both�the�causation�and�non-causation�based�lexico-grammatical�patterning�of�keyword�signals�for�the�Problem�element.�However,�as�emphasised�in�the�second�chapter�it�is�also�necessary�to�have�recourse�to�the�wider�context�to�explain�why�the�professional�or�students�writers�construct�the�discourse�as�they�do�and�this�aspect�has�also�been�referred�to�in�the�analysis�of�need.�The�following�chapter�describes�a�similar�analysis�carried�out�for�Evoking�and�Inscribed�signals�for�the�Solution�element�in�STUCORP,�but�within�a�more�functional�rather�than�notional,�i.e.�conceptual,�framework.�
chapter�8
STUCORPPhraseological�analysis�of�signals��for�the�Solution�element
The�analysis�of�Inscribed�and�Evoking�signals�in�STUCORP�for�the�Solution�ele-ment�is�based�on�the�functional�classificatory�framework�described�in�Chapter�6.�Five�Inscribed�signals,�recommendations, solution,�solutions, recommended�and�proposed,�will�be�analysed,�in�addition�to�the�Evoking�item�implementation.�As�in�the�previous�chapter,�the�focus�will�be�on�the�similarities�and�differences�in�the�patterning�of�these�signals�with�their�counterparts�in�PROFCORP.
Analysis of recommendations
Table�8-1�below�tells�us�that�the�Solution�element�of�the�Problem-Solution�pat-tern�is�realized�by�mainly�nominal�signals�in�STUCORP�(i.e.�recommendations, solutions,�solution),�but�by�adjectival�/�verbal�signals�in�PROFCORP�(i.e.�recom-mended, proposed).�What�is�striking�is�that�recommendations is�the�only�signal�which�occurs�as�a�key-key�word�in�both�STUCORP�and�PROFCORP.�
Table�4-1�has�shown�us�that�recommendations�occurs�as�a�keyword�in�five�reports�in�STUCORP�and�also�as�a�keyword�in�eight�of�the�reports�in�PROFCORP.�Of�the�39�tokens�for�recommendations�(excluding�11�headings�and�sub-headings)�in�STUCORP,�37�fall�into�the�category�of�‘Proposing�a�Solution’�and�only�two�into�
Table 8-1. In-text�tokens�for�Inscribed�signals�in�PROFCORP�and�STUCORP
Inscribed signals PROFCORPNo. of tokens
STUCORPNo. of tokens
Recommended *400 � � 57Proposed *584 � � 76Recommendations � *85 � *39Solutions � � � 9 � *89Solution � � 31 *118
*�Occurs�as�a�key�word�in�four�or�more�reports.
116� Corpus-based�Analyses�of�Problem-Solution�Pattern�
the�category�of�‘Evaluating�a�Solution’.�This�is�similar�to�PROFCORP�where�the�majority�of�the�tokens�for�recommendations�also�belong�to�the�category�of�‘Pro-posing�a�Solution’.�It�is�not�surprising�that�in�STUCORP�only�two�tokens�fall�into�the�‘Evaluating�a�Solution’�category�given�the�nature�of�these�reports,�as�these�are�recommendation-based�reports�suggesting�proposals�which�have�not�as�yet�been�implemented�and�therefore�not�yet�undergone�any�evaluation.�The�evaluative�ad-jectives�used�are�thus�based�on�the�writers’�own�analysis�of�the�situation�rather�than�any�hard�and�fast�evidence,�e.g.:
...we are able to propose some well grounded recommendations for improving the current …curriculum.
However,�the�lexico-grammatical�patterning�of�recommendations�in�STUCORP�is�quite�different�from�that�found�in�PROFCORP.�In�the�previous�section�it�was�noted�in�PROFCORP�that�the�active�and�passive�forms�of�the�verbs�collocating�with�recommendations displayed�a�preference�for�certain�sections�of�the�reports.�For�example,�verbs�such�as�‘present’�and�‘put�forward’�were�commonly�found�in�the�present�simple�active�in�the�Introductions,�e.g.�This report presents a summary of the main�findings and recommendations,�whereas�verbs�such�as� ‘make’,� ‘sum-marise’�and�‘provide’�in�the�present�simple�passive�form�tended�to�be�used�in�the�Body�of�the�reports,�e.g.�Recommendations are made….�However,�no�such�pat-terning�was�present�for�the�tokens�in�STUCORP.�
23�out�of� the�37�tokens�for�recommendations�are�used�in�the�Introduction�sections�of�the�reports,�of�which�15�are�found�with�verbs�in�the�active�(e.g.�‘make’,�‘give’)�and�8�with�verbs�in�the�passive.�With�the�frequent�use�of�the�active�voice,�it�is�not�surprising�to�find�the�interpersonal�pronoun�‘we’�used,�with�the�typical�lexi-co-grammatical�patterning�for�recommendations�shown�in�those�phrases�below:�
In this report, we will provide recommendations to increase residents’ awareness on…
We make recommendations for better project management.
This� aspect� of� interpersonality� was� distinctly� lacking� in� the� PROFCORP� data,�where�the�patterning�for�recommendations in�the�Introduction�sections�was�al-ways�of�the�type:�This report�presents�…
The�remaining�14�tokens�for�recommendations occur�in�the�Body�of�the�re-ports�and� �are�used�cataphorically.� Interestingly,� for�some�reason�or�other,� stu-dents�avoid�using�here�the�interpersonal�pronoun�‘we’�which�was�prevalent�in�the�Introductions.�Instead�we�find�the�passive�used,�e.g.�
Thus the following recommendations are given,
� Chapter�8.� STUCORP:�Solution�element� 117
Or�constructions�such�as:�
The following are some recommendations to improve the above problem
There are some recommendations for their improvement.
Although�the�above�are�grammatically�correct,�an�inspection�of�the�246�tokens�of�recommendations�in�the�Applied�Science�component�of�the�BNC�reveals�that�this�construction�does�not�occur,�thus�suggesting�that�it�is�more�typical�of�student�rather�than�professional�writing.�
Analysis of solutions and solution
Both� solutions� and� solution�occur�as�keywords� in� four�of� the� reports� in�STU-CORP,�thus�providing�further�evidence�that�students�are�heavily�assimilating�key�vocabulary� from�the�rubrics� for� this�writing�assignment� into� the�writing�up�of�their�own�recommendation�reports�(see�Appendix�3-2).�106�entries�are�recorded�for�solutions�of�which�89�occur�in�the�text�of�the�reports�as�opposed�to�being�used�as�(sub)-headings.�Of�these�89�in-text�tokens�for�solutions,�58�can�be�classified�under�‘Proposing�a�Solution’�and�31�under�the�category�of�‘Evaluating�a�Solution’,�which�are�both�analysed�in�detail�below.
Exactly�half�of� the�58� tokens� for� solutions in� the�category�of� “Proposing�a�Solution’�occur�in�the�Introduction�sections�of�the�reports.�In�this�section�‘we’�+�a�verb�in�the�active�is�used�in�35�cases�with�solutions,�as�in�the�examples�below,�but�it�is�not�used�exclusively�as�was�found�to�be�the�case�with�recommendations.
…we will finger out the actual problem and suggest solutions to CCST for improving …
…we will suggest some solutions and find out whether they are feasible.
Another�major�type�of�lexico-grammatical�patterning�in�the�Introductions�is�of�the�type:�
The goal / aim / purpose of this project / research / investigation is…
e.g. The goal of this research is to find out …and to suggest some solutions to this problem.
There�are�also�a�few�examples�of�a�verb�in�the�passive�with�variations�in�the�verbs�used,�e.g.�
Some proposed solutions to the problems are then outlined and…
118� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Although�the�students�are�using�a�wider�range�of�lexico-grammatical�patternings�here�than�those�found�with�recommendations,�their�use�of�lexis�is�quite�restricted.�For�example,� it� is� found�that�students�either�use� ‘suggest’�or� ‘recommend’�with�solutions.�Moreover,� it� is�also�obvious�from�all�the�above�examples�that�the�as-signment�rubrics�are�being�incorporated�into�the�discourse�through�the�constant�repetition� of� problem� /� problems,� which� is� found� to� collocate� overwhelmingly�with�the�verb�‘solve’�in�phrases�occurring�in�the�Introduction�sections�of�the�re-ports�in�STUCORP.
A�different�lexico-grammatical�patterning�exists�for�those�21�phrases�with�so-lutions�found�in�the�Body�of�the�reports,�although�the�juxtaposition�of�problem�/�problems�with�solutions�in�the�same�sentence�is�still�prevalent,�as�seen�from�the�examples�below.�The�most�common�type�of�pattern� is�one�where�solutions has�cataphoric�reference�and�is�used�in�a�kind�of�topic�sentence�as�in�the�examples�below:
There are several solutions recommended to solve the problem.
Owing to the importance of the problem mentioned in this report, five solutions are suggested here…
The�eight�phrases�with�solutions�occurring�in�the�Concluding�section�act�as�sum-mary�conclusions.�
Of�the�31�sentences�with�solutions�which�carry�some�kind�of�evaluation�three�contain�a�phrase�acting�as�a�two-way�signal�pointing�to�the�Solution,�such�as�the�one�below:
The above solutions can reduce the average waiting time for a student to find an unoccupied computer….
The�remaining�27�phrases�include�an�evaluative�adjective,�with�the�most�common�being�possible (13)�and�feasible�(10).�Moreover,�feasible,�in�addition�to�‘problem�/�problems’�and�‘situation’,�seems�to�be�another�lexical�item�from�the�rubrics�which�has�been�incorporated�by�students�into�their�writing.�However�these�phrases�in�which�solutions collocates�with�an�evaluative�adjective�do�not�have�quite�the�same�status�as�those�analysed�in�PROFCORP�where�an�evaluation�was�only�given�after�a�specific�solution�had�been�proposed�in�the�Body�of�the�reports.�In�the�STUCORP�data,�on�the�other�hand,�evaluative�adjectives�are�integrated�into�the�text�in�all�the�lexico-grammatical�phrases�analysed�under�‘Proposing�a�Solution’.�For�example,�in�the�Introduction�sections�possible�is�commonly�used,�whereas�feasible�tends�to�occur�in�the�topic�sentence�introducing�the�Body�of�the�reports,�e.g.:
� Chapter�8.� STUCORP:�Solution�element� 119
This report will analyse the problems in the system and suggest some possible solutions to resolve it.
As a result, we suggest some feasible solutions for the problem.
I�will�now�analyse�the�tokens�for�solution�in�STUCORP�and�compare�their�func-tions�and�patternings�with�those�for�solutions.�Of�the�118�tokens�for�solution�73�of�these�can�be�classified�as�‘Proposing�a�Solution’�and�45�as�‘Evaluating�a�Solution’.�In�the�category�of�‘Proposing�a�Solution’�the�majority�of�the�tokens�for�solution,�54�out�of�73, occur�in�the�Body�of�the�reports,�whereas�only�11�and�8�tokens�are�found�in�the�Introduction�and�Conclusion,�respectively.�One�common�function�of�phrases�with�solution�in�the�Introduction�is�the�specification�of�criteria:
In the economic aspect, the recommended solution should be cost-effective…
The solution should be financially supportable.
The�main�reason�why�the�majority�of�tokens�for�solution�occur�in�the�Body�of�the�reports�is�that�several�solutions�are�enumerated,�one�by�one,�using�such�phrases�as�the�following:
Another solution is to mass production of those common dishes.
The first solution is to freeze the tuition fee.
One solution is that student helpers may check the machine more frequently.
However,�nine�of�the�tokens�for�solution in�the�Body�also�refer�to�the�criteria�to�be�met,�which�overlaps�with�the�function�found�in�the�Introduction:
The solution should have some flexibility in the structure.
The�eight�tokens�in�the�Conclusion�sections�either�have�a�summarising�or�deduc-tive�function,�as�shown�below:
Increasing the resources is obviously a solution to the problem.
…so we highly recommend CCST to consider this solution in order to…
In�the�Conclusion,�it�is�to�be�noted�that�the�Theme/Rheme�patterning�is�different.�Whereas� in� the� Introduction�and�Body� sections� the�majority�of� the� tokens� for�solution are� thematised,�mainly� for� the�reason�that� they�are�preceded�by�some�kind�of�determiner�or�enumerative�adjective,�the�tokens�for�solution�are�found�in�Rheme�position�in�the�Conclusion.�
45�of�the�tokens�for�solution�can�be�classified�under�the�category�of�‘Evaluat-ing�a�Solution’�and�of�these�the�majority�(35)�are�found�in�the�Body�of�the�reports,�with�only�two�tokens�in�the�Introduction�and�eight�in�the�Concluding�sections.�
120� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Five�of�the�tokens�for�solution�are�not�evaluated�adjectivally,�but�by�other�gram-matical�constructions�such�as�adverbs�and�verbs�acting�as�two-way�signals�for�the�Problem-Solution�pattern:
This solution can effectively reduce the travelling time …
The�remaining�40�tokens�for�solution collocate�with�various�adjectives,�the�most�common�being�feasible (14),�best�(6)�and�possible�(4).�In�fact,�there�are�18�instances�of�a�superlative�form�of�an�adjective�being�used,�which�is�not�surprising�given�that�several�possible�solutions�are�discussed�in�the�Body�of�the�reports.�Out�of�the�35�tokens�for�solution�in�the�Body�sections,�11�carry�a�negative�evaluation,�indicating�rejection�of�a�proposed�solution,�e.g.:�…alternative 2 is not a technically feasible solution to the problem. If�we�have�a�look�at�the�rubrics�in�Appendix�3-2,�we�can�see�that�this�is�actually�a�response�to�one�of�the�guiding�questions�(no.�2).�In�this�respect,�see�Lea�and�Street�(1999)�who�consider�the�relationship�of�other�sites�of�textual�practice�such�as�guidelines� for�dissertation�writing� to�writing�processes�and�practices�in�the�academy.
The�Theme/Rheme�patterning�for�solution�in�the�‘Evaluating�a�Solution’�cat-egory�is�exactly�the�same�as�that�in�the�‘Proposing�a�Solution’�category.�Solution is�found�in�Theme�position�in�the�Body�of�the�reports,�e.g.�This solution is financially and technically feasible,�but�occupies�Rheme�position�in�the�Concluding�section,�e.g.�We find that the installation of surveillance camera in laboratories is a good solution.
This�analysis�of�solutions and�solution�in�STUCORP�has�shown�that�different�forms�of�a�lemma�not�only�have�a�different�colligational�patterning,�but�also�that�the�same�lemma�can�pattern�differently�depending�on�which�part�of�the�report�it�is�used�in.
Analysis of recommended
The�table�below�presents�a�breakdown�of�the�number�of�tokens�for�recommended and�proposed across�the�adjectival�and�various�verbal�categories�outlined�in�the�previous�section.�In�STUCORP�there�are�58�tokens�for�recommended,�of�which�only�one�is�used�as�part�of�a�sub-heading.�Of�the�remaining�57�tokens,�40%�occur�as�a�premodifying�adjective,�and�60%�as�some�kind�of�verbal�form,�which�are�an-alysed�below�according�to�their�lexico-grammatical�patternings.
� Chapter�8.� STUCORP:�Solution�element� 121
Recommended�as�premodifying�adjective
Recommended occurs�23�times�as�a�premodifying�adjective�and�its�use�is�restricted�to�collocation�with�a�narrow�range�of�nouns�in�STUCORP,�with�the�most�salient�being�‘solution’�(12)�and�‘system’�(9).�This�is�a�very�similar�use�to�that�found�in�PROFCORP�where�recommended was�also�found�to�collocate�with�a�few�specific�nouns�but�ones�related�to�monitoring�and�auditing.
An�examination�of�these�23�noun�phrases�shows�that�13�occur�in�some�kind�of�causal�relation�sentence.�One�pattern�to�emerge�from�the�data�is�a�concluding�statement�of�a�Grounds-Conclusion�type,�where�an�inference�is�made�based�on�previous�analysis:
Hence, the data implies that the recommended solution is accepted by various groups of people in the community.
Therefore, the recommended system is more economical than the present one.
In�contrast,�it�was�found,�that�in�the�PROFCORP�data�the�most�common�cause�relation�in�which�recommended�was�found�as�a�premodifying�adjective�was�Con-dition-Consequence.�Contextual�parameters�therefore�play�a�role�in�determining�the�type�of�causal�relation�in�which�recommended is�found�as�in�PROFCORP�the�focus�was�on�‘requirements�to�be�met’�whereas�in�STUCORP�the�topic�is�a�‘general�evaluation’�of�the�recommended�solution.
Recommended�in�impersonal�passive
Eight�of�the�57�tokens�for�recommended are�found�in�an�impersonal�passive�con-struction�(see�Table�8-2).� In�percentage� terms� this� is� fewer� than� the� tokens� for�recommended�in�PROFCORP,�but�this�difference�can�be�accounted�for�by�the�fact�
Table 8-2. In-text�tokens�for�the�adjectival�and�verbal�categories�of�recommended��and�proposed�in�STUCORP
Grammatical category Recommended ProposedNo. of tokens % of total No. of tokens % of total
Premodifying�adjective 23 � 40% 45 � 59%Impersonal�passive � 8 � 14% � 2 � � 3%Subject�+�passive 17 � 30% 16 � 21%Active � 5 � � 9% 10 � 13%Clause�construction � 4 � � 7% � 3 � � 4%Total 57 100% 76 100%
122� Corpus-based�Analyses�of�Problem-Solution�Pattern�
that�there�are�more�interpersonal�markers�with�the�use�of�active�voice�in�STU-CORP�(e.g.�we make some recommendations…).
All�these�eight�tokens�for�recommended are�involved�in�the�Grounds-Con-clusion causal�relation.�In�four�of�the�cases�this�relation�is�explicitly�signalled�by�adverbs�such�as�‘therefore’�and�‘thus’,�e.g.:
� � Therefore, it is strongly recommended that a urgent development programme on …should be carried out.
Thus, it is recommended that CCST should provide more hardware support to that platform.�
However,�in�the�other�four�cases,�as�I�have�argued�in�the�previous�section�in�the�discussion�of�this�grammatical�construction�in�PROFCORP,�the�Grounds-Con-clusion relation,�while�not�explicitly�signalled,�is�implied�in�the�text�by�virtue�of�the�positioning�of�this�lexico-grammatical�patterning�in�the�overall�discourse.�For�example,�the�sentence�below�occurs�in�the�last�part�of�the�report�and�arrives�at�a�conclusion�based�on�analysis�of�evidence�provided�in�the�Body�of�the�report.
It is recommended that those large laboratories should install this system in order to safe guard the precious properties inside.
In�this�respect,�the�student�use�of�recommended�in�impersonal�passive,�when�it�occurs,�is�similar�to�the�professional�use.�
Recommended�in�passive�+�subject�construction
The�passive�+�subject�construction�is�used�17�times�and�accounts�for�30%�of�the�tokens� for�recommended� in�STUCORP,�which� is�very� similar� to� its�percentage�distribution� in� this�grammatical�category� in�PROFCORP�(34%).� It� is� in�nouns�as�subject�where�the�main�differences�between�STUCORP�and�PROFCORP�lie.�It�was�noted�in�PROFCORP�that�one�category�of�these�subject�nouns�comprised�nominalisations�(e.g.�The utilisation of quietened equipment is recommended…).�However,� such� nouns,� which� very� often� happen� to� be� grammatical� metaphor�nouns,�were�not�found�in�this�data.�In�a�few�cases,�students�had�used�a�gerund�(e.g.�installing)�where�a�grammatical�metaphor�(e.g.�Installation)�could�have�been�used�instead,�e.g.:
Installing computer terminals is then highly recommended over building a new computer barn.
A�cross-check�with�the�Applied�Science�component�of�the�BNC�reveals�that�instal-lation�occurs�more�frequently�(448�tokens)�than�installing�(153�tokens).�Although�
� Chapter�8.� STUCORP:�Solution�element� 123
installing is�acceptable�usage,�the�BNC�data�do�suggest�that�there�is�a�preference�for�the�nominalisation�form�over�the�gerund�for�this�particular�verb.
Recommended�in�active�voice
There�are�only�five�tokens�for�recommended�in�the�active�voice,�which�are�all�in�the�past�tense.�However�the�use�of�the�past�tense�differs�from�that�in�PROFCORP�where�it�refers�to�recommendations�made�in�a�previous�document�and�occurs�in�the�Introduction�section�of�the�reports.�In�STUCORP,�recommended is�found�in�the�Body�of�the�reports�where�it�is�used�for�reporting�respondents’�opinions�of�the�survey�questions.
Recommended�in�other�clause�constructions
Only� four� tokens� for�recommended were�recorded�as�part�of�other�clause�con-structions,�of�which�three�were�reduced�clauses.�There�were�no�examples�of�the�phrase�‘as�recommended’,�which�was�found�in�PROFCORP�as�an�intertextual�de-vice,�referring�to�a�previous�study�or�report�(e.g.�…as recommended in EPD Con-taminated Spoil Management Study…).
Analysis of proposed
There�are�84�tokens�for�proposed in�STUCORP,�eight�of�which�function�as�part�of�a�sub-heading,�combining�with�‘system’�in�five�cases.�The�76�tokens�found�in�the�text�of�the�reports�are�analysed�below�according�to�the�grammatical�categories�outlined�in�Table�8-2.
Proposed�as�premodifying�adjective
The�majority,�45�out�of� the�76� tokens�of�proposed� (i.e.�59%),� fall� into� this� cat-egory.�Likewise,�in�PROFCORP�the�majority�of�the�tokens�of�proposed�were�also�found�in�this�category.�A�comparison�with�the�tokens�for�recommended used�as�a�premodifying�adjective�in�STUCORP�reveals,�however,�some�differences�in�fre-quency�and�usage.�First,�proposed occurs�more�frequently�than�recommended�in�STUCORP,�with�45� tokens�recorded� for�proposed as�opposed� to�23� for�recom-mended.�The�reason�for�this�may�be�that�recommended�is�mainly�used�with�only�two�nouns:�solution�(12)�and�system (9).�Proposed,�on�the�other�hand,�is�found�
124� Corpus-based�Analyses�of�Problem-Solution�Pattern�
to�occur�with�a�greater�range�of�nouns�(e.g.�modification,�policy,�design,�activity,�strategy,�process,�scheme).�
Not�only�are�there�differences�in�collocation�between�proposed and�recom-mended in�STUCORP,�but�also�differences�in�their�functions�in�the�overall�dis-course.� Whereas� it� was� noted� that� collocations� with� recommended were� often�found�in�a�Grounds-Conclusion type�of�relation�in�the�latter�part�of�the�reports,�the�collocations�with�proposed�were�mainly�distributed�between�the�Introduction�and�Methodology�sections�of�the�report�(cf.�Gledhill�1995,�2000).
Proposed�in�impersonal�passive
There� is� only� one� example� of� the� lexico-grammatical� patterning� it is proposed that…�and�one�example�of�proposed in�the�impersonal�passive�followed�by�a�Pur-pose clause�in�the�Rheme�part�of�the�sentence:
It is proposed to increase the number of credits for Final Year Project to 6 to solve the problems mentioned above.
Proposed�in�subject�+�passive�construction
An�examination�of�the�16�tokens�for�proposed in�the�passive�+�subject�construc-tion�shows�that�of�these�are�all�in�Rheme�position,�with�two�of�the�tokens�preced-ed�by�a�Purpose clause�as�Theme,�and�13�by�a�textual�theme,�as�in�the�following�examples:
To address the stated problem a censorship system is proposed…
Therefore, the following recommendations are proposed.
Although�the�above�patterning�is�very�similar�to�that�found�in�PROFCORP,�sev-eral�of�the�sentences�are�rather�awkward�sounding�because�of�the�subjects�of�the�sentence.�The�patterning�‘it�is�proposed�that�…’�would�sound�more�native-like�in�the�sentences�below:
The new convenience store is proposed to open from 2pm to 2am.
10 sets of computer terminals are proposed to be placed in Academic Concourse.
In�PROFCORP�it�was�found�that�this�construction�employed�nominalisations�in�the�form�of�grammatical�metaphor�nouns,�e.g.�‘utilisation’,�‘option’,�but�such�nouns�were�lacking�in�student�writing.�
� Chapter�8.� STUCORP:�Solution�element� 125
Proposed�in�active�voice
Again,� there� are� very� few� tokens� of� proposed� in� this� category,� but� one� or� two�points�merit�a�mention.�All�of�the�10�tokens�take�‘we’�as�the�subject.�Proposed is�mainly�found�in�the�Conclusion�section�of�the�reports�where�it�always�signals�a�Grounds-Conclusion�relation:
Therefore, we proposed that CCST should make an announcement to the public to ask the user to be considerate …
However,�proposed�also�occurs�in�the�Introduction�section,�setting�out�the�back-ground,�e.g.:
We have proposed four different schemes to be evaluated.
These�learner�writers�are�therefore�making�a�distinction�between�recommended�and�proposed in�the�active,�as�recommended is�reserved�for�reporting�the�respon-dents’�opinions�in�the�Body�of�the�reports�and�none�of�the�five�tokens�for�recom-mended takes�‘we’�as�the�subject.�Moreover,�‘we’�is�never�found�with�either�recom-mended�or�proposed�in�the�active�in�PROFCORP.�Either�the�impersonal�passive�is�used�or�the�passive�+�subject,�both�of�which�seem�to�have�the�effect�of�distancing�the�writer� from� the� recommendations�presented� in� the�environmental� reports,�and�can�therefore�be�viewed�as�typical,�formulaic�writing�style�for�these�kinds�of�reports�in�the�Hong�Kong�context.�
Proposed�in�other�clause�constructions
There�are�only�three�tokens�for�proposed where�the�learner�writers�have�attempted�to�use�it�in�the�form�of�a�reduced�relative�clause.�Two�of�these�are�used�with�an�agent�(...the FYP topics proposed by the lecturers),�but�the�other�one�below�sounds�distinctly�odd,�and�would�be�more�natural�substituted�by�a�premodifying�adjec-tive�(e.g.�‘since�the�proposed�smart�card…’).
… since the smart card proposed would be accepted by all the photocopiers on campus…
A�comparison�with�the�analysis�of�the�tokens�for�recommended�and�proposed�as�reduced�relative�clauses�in�PROFCORP�reveals�that�these�are�always�accompanied�by�some�form�of�postmodification,�usually�a�prepositional�phrase,�e.g.�…controls recommended in this report….
126� Corpus-based�Analyses�of�Problem-Solution�Pattern�
I�now�turn�to�an�analysis�of�one�Evoking�item�and�see�how�it�compares�across�PROFCORP�and�STUCORP.
Analysis of implementation
There�are�six�evoking�items�in�PROFCORP,�which�occur�as�keywords�in�four�or�more�reports.�Out�of�these�implementation has�been�chosen�for�analysis�because�it�is�the�only�one�of�the�Evoking�items,�which�also�occurs�in�STUCORP�(44�to-kens)�and�also�because�it�is�the�Evoking�item�which�has�the�most�general�mean-ing,�as�explained�previously.
The�distribution�of�the�tokens�for�implementation�across�the�functional�cat-egories�in�STUCORP�is�quite�different�from�that�in�PROFCORP.�First�of�all,�out�of�the�44�tokens�for�implementation�in�STUCORP�17�are�used�as�headings�or�sub-headings,�whereas�only�three�out�of�133�tokens�of�implementation�in�PROFCORP�were�used�in�this�way.�However,�this�is�not�surprising�as,�in�accordance�with�the�assignment�guidelines,�students�are�expected�to�discuss�various�implementation�issues�of�their�proposed�recommendations.�Of�the�remaining�27�tokens,�though,�only�three�can�be�classified�as�‘Evaluating�a�Solution’,�with�24�of�the�tokens�belong-ing� to� the� ‘Proposing�a�Solution’� category.�This� is�quite�a�different�distribution�pattern�to�those�in�PROFCORP�where�half�of�the�130�tokens�of�implementation could�be�classified�as�‘Evaluating�a�Solution’.�
Most�of�the�24�tokens�in�the�‘Proposing�a�Solution’�category�are�found�in�the�Body�section,�elaborating�on�implementation�of�the�proposal:
The actual implementation is briefed as follows: firstly, the Department announces all FYP topics proposed by the lecturers.
Of�the�three�tokens�for�implementation which�are�involved�in�evaluation,�all�em-ploy�the�interpersonal�marker�‘we’,�which�was�never�used�to�introduce�an�evalu-ation�in�PROFCORP.�Moreover,�only�one�of�these�phrases�with�implementation�resembles�the�second�type�of�lexico-grammatical�patterning�with�a�two-way�sig-nalling�verb�discussed�in�the�previous�section,�as�given�below:
...we suggest that the implementation of a formal laboratory safety course is a necessity to alleviate the current situation.
Given� that� one� of� the� main� objectives� of� these� recommendation� reports� is� to�persuade�the�reader�of�the�value�of�the�project,�it�would�seem�that�students�are�unaware� of� highlighting� the� benefits� of� their� proposal� through� the� two� causa-tion-related�lexico-grammatical�patternings�which�were�very�much�in�evidence�
� Chapter�8.� STUCORP:�Solution�element� 127
in�the�PROFCORP�analysis.�However,�by�searching�on�implement*�in�STUCORP�I�found�that�in�a�few�cases, students�did�attempt�to�give�a�positive�evaluation�of�their�proposed�plan,�but�in�a�very�clumsy�way.�To�illustrate�this�point�I�will�take�the� following� typical� sentence� from� STUCORP,� which� although� grammatically�correct,�is�‘student�writing’�and�not�‘proper�writing’,�as�one�of�my�students�put�it.�
� (a) Implementing our proposed changes is also highly welcome for students and outside firms and we can have a more effective curriculum preparing students for their future.
We�can�see�that�the�above�is�a�rather�wordy,�long,�complex�sentence.�In�fact,�Hin-kel’s�(2002)�corpus-based�research�of�students’�academic�writing�has�revealed�this�same�overuse�of�phrase�level�coordinators�such�as�‘and’�and�‘but’.�
A�change�in�the�sentence�structure�of�the�above�example�to�either�of�the�two�following�alternatives,�modelled�on�structures�prevalent�in�PROFCORP,�gives�us�the�following:
� (b) Implementation of our proposed changes, which would also be highly welcomed by students and outside firms, would ensure a more effective curriculum to prepare students for their future.
� (c)� With the implementation of our proposed changes… our curriculum would be more effective.
Now,�if�we�compare�the�student�writing�in�(a)�with�the�suggested�reformulations�in�(b)�and�(c),�we�can�see�that�the�student�writing�is�more�reminiscent�of�spoken�rather�than�formal�written�language.�By�way�of�example,�Halliday�(2002)�gives�the�following�sentence�as�an� instance�of�written� language�with� its� spoken�“transla-tion”,�so�to�speak.�
� � Investment in a rail facility implies a long term commitment.�(written)
If you invest in a facility for the railways you will be committing [funds] for a long time.�(spoken�equivalent)
Halliday�(2004)�has�remarked�that�a�corpus�of�written�language�is�set�up�lexically,�and�is�therefore�much�easier�to�analyse.�On�the�other�hand,�spoken�language�tends�to�favour�the�grammatical�over�the�lexical�with�long�clause�complexes�occurring�in�speech,�as�in�Halliday’s�example�above,�and�also�in�my�example�of�student�writ-ing.�Because�of�its�grammatical�intricacy,�Halliday�maintains�that�a�corpus�of�spo-ken�language�is�more�difficult�to�analyse�than�a�corpus�of�written�data.�This�is�an�extremely�interesting�observation,�which�has�been�borne�out�by�the�above�data�in�STUCORP.�It�also�suggests�that�the�distinctions�between�writing�and�speak-ing�may�not�be�so�clear-cut�in�cases�of�learner�corpora�of�written�data�and�that�in�
128� Corpus-based�Analyses�of�Problem-Solution�Pattern�
some�instances�where�learner�corpora�display�features�of�spoken�language,�they�could�be�viewed�more�as�a�‘hybrid’�corpus�of�written�and�spoken�language.�
Conclusion
The�most�striking�observations�regarding�the�lexico-grammatical�patternings�of�the�key�words�for�the�Solution�element�in�STUCORP�is�that�they�rely�quite�heav-ily�on�the�metalanguage�of�the�rubrics�and�guidelines�for�the�project,�as�has�been�noted�in�several�places�in�the�foregoing�analysis.�Students�did�demonstrate�writ-ing�proficiency,�though,�in�their�use�of�topic-like�sentences�and�overall�summary�conclusions.�
There�was�also� far�more� interpersonal�use�of� the�pronoun� ‘we’,� e.g.�we will�provide recommendations�…�in�the�student�reports,�which�was�not�in�evidence�in�PROFCORP�with�a�similar�proposition�being�expressed�by�the�impersonal�pas-sive,�e.g.�it is recommended / proposed that�….�However,�due�to�the�different�eth-nographic�situations�in�which�the�STUCORP�and�PROFCORP�reports�are�con-structed�(see�Chapter�3)�these�different�registers�are�entirely�appropriate.�
chapter�9
General conclusions and implications for pedagogy
In�this�concluding�chapter�I�will�first�consider�the�major�findings�revealed�by�the�analysis�of�PROFCORP�in�light�of�the�general�objectives�outlined�at�the�end�of�Chapter�2.�I�will�then�summarise�the�main�similarities�and�differences�between�PROFCORP�and�STUCORP�from�a�discourse-based�perspective�and�also�at�the�sentence-level� to�highlight� specific� features�of�apprenticeship�writing.�Finally,� I�will�discuss�the�pedagogic�applications�of�these�findings.
Some principal findings from PROFCORP
It�is�now�well�established�in�the�literature�that�language�is�made�up�of�interlocking�phraseological�units,�i.e.�lexico-grammatical�patterns�(see�Hoey�1991,�2005;�Sin-clair�1991;�Hunston�2001;�Hunston�&�Francis�2000;�Stubbs�1996),�although�what�proportion�of�language�constitutes�these�units�is�open�to�debate.�Where�this�book�has�attempted�to�advance�the�field�of� lexico-grammatical�patterning�is� through�examining� this� language� phenomenon� within� a� discourse-based� framework� of�notions�and�functions�for�a�specific�rhetorical�pattern,�namely�the�Problem-Solu-tion�pattern.�A�review�of�some�of�the�key�findings�in�PROFCORP,�which�seek�to�answer�the�questions�posed�in�Chapter�2,�follows.
As�far�as�the�Problem�element�is�concerned,�phrases�signalling�this�element�have� been� shown� to� be� heavily� involved� in� causal� relations,� most� notably� the�Reason-Result�and�Means-Purpose�relation.�What�is�most�significant�about�the�markers�of�these�causal�relations�is�not�that�they�are�typically�some�type�of�con-nective,�e.g.�‘As�a�result…’,�‘Therefore…’,�but�are�overwhelmingly�lexical�in�nature�in�the�form�of�explicit�and�implicit�causative�verbs.�Another�significant�finding�is�that�prepositions,�especially�‘with’�and�‘from’,�also�seem�to�be�acting�as�signals�of� causality,� as� does� the� more� mitigating� phrase� ‘associated� with’� (as� shown� in�Chapter�7).�
It�has�also�been�suggested�that�the�status�of�a�lemma�can�change�depending�on�whether�it�enters�into�a�causal�relation�or�is�non-causation�based.�For�example,�problem,�a�Vocabulary�3� item�which� is�viewed�as�a�common�A-Noun,�has� this�
130� Corpus-based�Analyses�of�Problem-Solution�Pattern�
function�mainly�in�causation-related�phrases�(see�Chapter�7).�Another�keyword�signal�impacts investigated�in�Chapter�5�seems�to�be�functioning�as�a�synonym�of�problems�in�causation-related�phrases,�but�changes�its�status�to�one�of�hyponym�in�relation�to�problems�when�it�occurs�in�non-causation�phrases.�These�prelimi-nary�findings� indicate� that�more� research� into� looking�at�how�discourse-based�notional� categories� affect� the� status� and� the� relationship� of� a� lexical� item� with�other�items�would�be�worth�pursuing.
In�the�analysis�of�key�words�for�the�Solution�element,�the�systemic�functional�category�of�grammatical�metaphor�noun�(e.g.�implementation)�was�found�to�be�in-strumental�in�signalling�the�Solution�element�in�PROFCORP.�Such�nouns,�which�are�a�feature�of�formal�scientific�writing,�were�found�to�occur�in�different�lexico-grammatical�patterns�depending�on�their�overall�function�in�the�discourse.�
Aspects�which�concern�genre�analysts,�such�as�intertextuality�(the�linguistic�traces�of�other�texts)�and�interdiscursivity�(rhetorical�conventions�borrowed�from�other�texts),�have�also�been�touched�on�(Flowerdew�2008a).�It�was�suggested�in�Chapter�5�that�‘associated�with’�was�used�as�a�hedging�device�for�expressing�cau-sality�in�view�of�the�fact�that�this�verb�phrase�with�a�negative�semantic�prosody�was�found�to�occur�in�texts�from�all�23�companies,�thus�indicating�it�was�a�rhe-torical�feature�of�this�particular�genre,�and�possibly�of�science�writing�in�general.��
Examination� of� the� keyword� signals� from� a� grammatical� basis� within� the�broad�function�of�‘Proposing�a�Solution’�has�uncovered�the�intertextuality�of�the�PROFCORP�documents�and� their� relationship� to�other� related�documentation�through� such� phrases� as� recommendations made in…� and� the� use� of� a� specific�body�or�organization�with�a�keyword�verb�in�the�present�perfect,�e.g.�The EIA has recommended that ….
Such�ethnographic�considerations�alert�us�to�the�fact�that�in�order�to�fully�and�accurately�“interpret”�the�lexico-grammatical�patternings,�the�role�that�contextual�features,�outlined�in�Chapter�2,�play�in�shaping�the�discourse�has�also�to�be�taken�into� account.� For� this� reason,� Widdowson� (1998,� 2002)� maintains� that� corpus�data�are�but�a� sample�of� language,�as�opposed� to�an�example�of�authentic� lan-guage,�because�it�is�divorced�from�the�communicative�context�in�which�it�was�cre-ated;�‘the�text�travels�but�the�context�does�not�travel�with�it’.�This�is�an�important�observation�and�can�create�dilemmas�for�the�analyst�in�corpus�interpretation.�
This�is�where�I�see�the�value�of�working�with�small-scale�specialized�corpora�such�as�the�kind�investigated�in�this�book�and�also�those�reported�on�in�Ghadessy�et�al.�(2001)�where�the�analyst�is�probably�also�the�compiler�and�does�have�famil-iarity�with�the�wider�socio-cultural�dimension�in�which�the�discourse�was�cre-ated�(Flowerdew�2004a).�It�also�means�that�ultimately�doing�small-scale�corpus�linguistics�is�different�from�doing�large-scale�corpus�linguistics.�Investigations�of�large-scale�corpora�can�give�us�valuable�insights�into�broad-based�collocational�
� Chapter�9.� General�conclusions�and�implications�for�pedagogy� 131
and�colligational�patterning:�for�example,�a�trawl�through�the�246�tokens�of��‘rec-ommendations’�in�the�Applied�Science�component�of�the�BNC�reveals�that�‘rec-ommendations’�frequently�occurs�with�the�verb�‘make’�but�in�three�very�different�lexico-grammatical�patternings:�
� � To highlight the salient points, the following recommendations are made.
We have made various recommendations for strengthening…
This document makes several specific recommendations on pay.
Finer�tunings�of�lexico-grammatical�patternings�I�have�referred�to�as�lexical�colli-gations.�But�which�grammatical�company�that�the�collocation�‘recommendations�+�make’�keeps�for�a�particular�context�may�only�be�available�to�an�analyst�who�is�familiar�with�the�socio-contextual�features�of�a�small�corpus�and�not�recoverable�from�the�concordance�lines�drawn�from�a�large-scale�corpus.
Corpora�are�thus�delimited�by�ethnographic�considerations�such�as�intertex-tuality,� interdiscursivity�and�registerial�constraints,� involving�choices� like� inter-personal�‘we’�vs.�impersonal�passive,�or�technical�vs.�non-technical�lexis,�as�well�as� text-type� (Problem-Solution� pattern,� in� this� case),� which� themselves� are� all�factors�in�determining�the�specific�lexico-grammatical�patternings.
Expert vs. apprentice writing
Investigation�of�the�lexico-grammatical�patterning�of�key�words�for�the�Problem�and�Solution�elements�in�STUCORP�has�uncovered�areas�where�students�show�themselves�to�be�proficient�and�areas�where�they�are�deemed�less�proficient.
The�analyses�in�Chapters�7�and�8�have�led�me�to�conclude�that�students�seem�to�be�quite�adept�at�structuring�their�overall�argument�within�the�Problem-Solu-tion�based�pattern.�They�show�mastery�of� lexico-grammatical�patterning�at� the�macrostructure�level�for�using�topic-like�sentences�for�introducing�the�problems,�e.g.�There are several problems…,�and�then�enumerating�these�in�follow-up�sup-porting�sentences,�e.g.�The first problem is… (Chapter�7). The�analyses�also�show�that� the� development� of� the� argument� unfolds� quite� logically.� This� is� apparent�from�Tables�4-1�and�4-2�showing�the�keyword�Inscribed�and�Evoking�items�for�the�pattern�and�also�the�investigation�of�the�key�word�need� in�Chapter�7.�First,�students� establish� the� existence� of� a� problem. Moreover,� this� Problem� element�is�very�often�focused�on�some�kind�of�shortcoming,�which�explains�why�insuffi-cient�surfaces�as�a�keyword�Evoking�item,�e.g.�… number of available computers in barns becomes really insufficient. Then�either�there�is�a�kind�of�prefacing�statement�to�Hoey’s�Recommended�Response� stage� (see�Chapter�1),� e.g.�…the number of
132� Corpus-based�Analyses�of�Problem-Solution�Pattern�
machines cannot meet the need of students;�or,�we�have�a�Recommended�Response,�e.g.�Therefore, there is a need to provide more payphones.�
However,�students’�use�(or�possibly�overuse?)�of�topic�sentences�can�be�ex-plained�by� the� fact� that� the�course�materials� in� the�first�year�overtly� teach� this�signalling�aspect�of�writing.�Also,� the�report�writing�guidelines� for� this�project�contain�several�expository-type�exercises�for�structuring�the�content�of�the�report,�which�provide�students�with�the�basic�macrostructure�(see�Appendix�3-1).�It�thus�appears�that�student�have�assimilated�into�their�reports�the�metalanguage�given�in�the�writing�rubrics�and�guidelines,�which�has�been�shown�to�permeate�the�dis-course�of�the�STUCORP�reports,�one�very�common�overused�patterning�being�variations�of��‘solutions�to�the�problem’.�The�influence�of�input�material�here,�in�the�form�of�writing�guidelines,�is�therefore�an�important�consideration�in�analyses�of�learner�corpora.�Other�researchers�(see�Milton�2000;�McEnery�&�Kifle�2002)�have�also�noted�the�influence�of�coursebook�material,�specifically�the�expressions�of�doubt�and�certainty,�on�student�writing.�
Sentence-level� formal� errors� have� been� exposed,� one� such� error� being� the�misuse�of�‘It’,�which�is�specifically�related�to�the�interlanguage�of�Hong�Kong�Chi-nese�learners�of�English;�for�instance,�the�substitution�of�existential�‘there’�by�‘It’,�e.g.�It is no need to explain.�However,�what�seems�to�distinguish�apprentice�writers�from�professional�writers�is�not�so�much�these�sentence-level�formal�errors,�but�rather�other�types�of�deficiencies�related�to�expressing�causal�relations,�as�sum-marized�below.�
Although�students’�writing�may�be�grammatically�correct�for�the�most�part,�this� corpus-based� analysis� has� uncovered� several� areas� where� students’� writing�‘doesn’t�sound�quite�right’,�for�want�of�a�better�expression,�or�unidiomatic�because�they�lack�the�necessary�lexico-grammar�for�expressing�their�ideas.�In�many�cases,�students�seem�to�be�circumventing�their�lack�of�appropriate�patterns�by�using�what�language�means�they�have�at�their�disposal�and�coming�up�with�phrases�where�the�essential�meaning�can�be�understood,�but�‘we�wouldn’t�express�it�like�this�is�Eng-lish’,�such�as�in�the�phrase�…have a very good policy against the problem.
Sentences�such�as�the�one�above�would�seem�to�arise�from�the�fact�that�the�student�writing�was�found�to�be�wanting�especially�in�the�area�of�verbs,�with�stu-dents�exhibiting�a�very�narrow�range�of�these�verbs,�confusing�cause/effect�with�result/effect�verbs�and�displaying�inadequate�command�of�ergative�verbs�for�the�Problem�element.�The�verbs�used�for�expressing�the�Solution�element�were�also,�on�occasions,� registerially� inappropriate,� e.g.�get rid of.�Grammatical�metaphor�nouns�(e.g.� implementation),�which�are�a� feature�of� formal�science�writing�and�predicted�certain�‘two-way’�signalling�verbs�in�PROFCORP�linking�the�Problem�and�Solution�elements,�of�the�pattern:�implementation of�+�Solution�would allevi-ate� +� Problem,� were� largely� absent� in� STUCORP.� Lack� of� familiarity� with� this�
� Chapter�9.� General�conclusions�and�implications�for�pedagogy� 133
pattern,�and�indeed�others�involving�nominalizations,�could�largely�explain�why�students�writing� resembled�more� spoken� language� in�places�with� ‘and’�used� to�join�clause�complexes.�
In�sum,�the�types�of�deficiencies�observed�in�student�writing�are�not�so�much�sentence-level�formal�errors,�but�rather�indicate�the�lack�of�an�appropriate�gram-mar� system� and� vocabulary� range� which� play� a� role� in� the� co-construction� of�meaning�through�the�blending�of�collocational�and�colligational�features�of�lan-guage�in�the�type�of�discourse�under�investigation�in�this�book.�This�is�the�kind�of�student�writing�de�Beaugrande�(2001:�10)�is�singling�out�when�he�writes�‘Our�ma-jor�problem�is�not�so�much�bad English�or�incorrect English,�as�is�often�lamented,�but�rather�insufficient English’.
Pedagogic implications and applications of findings
Most�of�the�language�exercises�in�coursebooks�on�technical�writing�focus�on�sen-tence-level� grammar� errors� such� as� use� of� tenses� and� formation� of� the� passive�voice.�While�not�denying�the�value�of�such�exercises,�the�analyses�in�Chapters�5,�6,�7�&�8�have�demonstrated�that�more�language�work�needs�to�be�devised�on�lexico-grammatical�patterning�in�order�to�bring�apprentice�writers�up�to�the�level�of�pro-fessional�writers.�It�is�not�just�a�question�of�using�the�passive�voice�correctly,�but�as�the�analyses�have�revealed,�using�appropriate�lexicogrammatical�patternings�with�consideration�of�various�contextual�and�situational�features�of�the�discourse�for�the�notions�and�functions�one�wishes�to�convey:�causality�and�proposing�/�evalu-ating�a�solution�being�the�respective�categories�under�investigation�in�this�book.�Such� examples� would� be� the� combination� of� appropriate� nominalizations�with�the�passive�voice�or�the�juxtaposition�of�prepositions�with�grammatical�metaphor�nouns�e.g.�With the implementation of…to�signal�the�Solution�and�Evaluation�ele-ments,�particularly�in�a�concluding�section�of�the�text.�
The�findings�and�implications�for�pedagogy�derived�from�applied�linguistics�work�in�phraseology�(Groom�2005;�Hunston�2003;�Jones�&�Haywood�2004;�Sin-clair�(ed.)�2004;�Wray�1999,�2000)�are�now�beginning�to�filter�through�into�the�teaching�community�(see�Flowerdew�2001,�2002;�Tribble�2001;�Willis�2003).�In�a�state-of-the-art�article�on�ESP,�Belcher�(2006)�remarks�on�the�increasingly�main-stream�role�that�corpora�are�now�playing�in�enhancing�ESP�literacy.�Needless�to�say,�one�of�the�major�challenges�for�textbook�writers�in�the�next�few�years�will�be�how� to� exploit� and� “translate”� corpus-based� findings� of� discourse-based� phra-seological�units�into�comprehensible�input�for�learners.�In�any�case,�Widdowson�(1998,�2002)�views�the�transference�of�corpus�data�to�pedagogy�as�problematic�due�to�the�decontextualised�nature�of�corpus�data�and�questions�how�such�data�
134� Corpus-based�Analyses�of�Problem-Solution�Pattern�
can�be�transformed�from�samples�to�examples�of�language,�i.e.�how�can�students�authenticate�the�corpus�data�to�suit�the�socio-cultural�and�linguistic�parameters�of�their�local�environment?�However,�other�linguists�would�seem�to�disagree�that�this�is�a�major�hurdle�to�be�overcome,�with�Sinclair�(2002)�proposing�some�kind�of�‘pedagogic�processing’�stage�to�make�the�data�intelligible�to�learners�and�McCar-thy�(2002)�objecting�on�the�grounds�that�Widdowson�underestimates�the�ability�of�students�to�change�samples�into�examples.�Again,�this�is�where�I�see�the�value�of�exploiting�small� ‘localised’�expert�corpora�for�pedagogic�purposes;� the�more�the� corpus�draws�on�contextual� features� from� the� students’�own� socio-cultural�environment,�the�easier�it�should�be�for�the�teacher�to�act�as�a�kind�of�mediating�‘ethnographic�specialist�informant’�of�the�raw�corpus�data,�thereby�authenticating�the�data�for�classroom�use�to�fit�the�students’�reality.�
Another�way�forward�in�this�area�is�the�compilation�of�local�learner�corpo-ra,�similar�to�the�one�described�in�this�book,�as�advocated�by�Mukherjee�(2006),�Mukherjee�&�Rohrbach�2006)�‘…the�exploration�of�learner�data�by�the�learners�themselves�will�motivate�many�more�learners�to�reflect�on�their�language�use�and�thus�raise�their�foreign�language�awareness’.�See�Nesselhauf�(2003,�2004b)�for�a�review�of�the�applications�of�learner�corpora�to�language�teaching�and�useful�sug-gestions�for�the�exploitation�of�learner�corpora�in�data-driven�learning�(DDL).�
Based�on�the�deficiencies�uncovered�through�a�comparison�of�PROFCORP�and� STUCORP,� below� I� outline� a� few� suggestions� for� DDL� exploiting� various�search�engines,�with�reference�to�some�of�the�points�raised�in�the�literature�on�the�corpus-based�approach.�
Applications�of�corpora�in�data-driven�learning:�Some�critical�points
I�have�noted�the�importance�of�a�lexico-grammatical�orientation�to�corpus�analy-sis�in�this�book,�and�would�like�to�reiterate�the�application�of�this�perspective�to�DDL,�an�approach�that�is�inherent�in�the�tasks�proposed�by�Gavioli�(2005)�with�their�focus�on�collocation,�colligation,�semantic�prosody�and�semantic�preference.�However,�this�approach�is�not�without�its�problems,�which�are�also�raised�in�Gav-ioli’s�book�and�summarized�by�Meunier�(2002).
Despite� their�advantages,�DDL�activities�have� some�drawbacks:� they�are� time-consuming�(because�of�the�interaction,�negotiation�and�research�procedure�ad-opted�by�the�students)�and�also�require�a�substantial�amount�of�preparation�on�the�part�of�the�teacher,�who�has�to�predefine�the�forms�that�will�be�focused�on�and�make� sure� that� interesting� teaching�material� is�provided.�The�various� learning�strategies�(deductive�vs.�inductive)�that�students�adopt�can�also�lead�to�problems.�
� Chapter�9.� General�conclusions�and�implications�for�pedagogy� 135
Some�students�hate�working�inductively�and�teachers�should�aim�at�a�combined�approach�(see�Hahn�2000�for�a�combined�approach).�� (Meunier�2002:�135)
In� the� exposition� of� some� lexico-grammatical� concordance� activities� below,� I�would� like� to� take� up,� in� particular,� two� other,� not� necessarily� drawbacks,� but�rather�considerations,�of�a�DDL�approach.�One�issue�relates�to�Widdowson’s�point�that�students’�need�to�authenticate�the�corpus�data�to�fit�the�contextual�environ-ment� of� their� own� writing.� In� this� regard,� I� will� come� back� to� a� finding� from�Chapter�8,�namely�the�lack�of�grammatical�metaphor�nouns,�e.g.�implementation,�in�student�writing�(see�Mohan�&�Huxur�2001).�I�suggested�that�in�a�recommenda-tion�report�proposing�modifications�to�the�existing�curriculum,�student�writing�could�be�reformulated�along�the�following�lines,�linking�the�solution�and�evalua-tion�via�an�implicit�causative�verb�instead�of�‘and’.
� � Student�writing: Implementing our proposed changes is also highly welcome for students and
outside firms and we can have a more effective curriculum preparing students for their future.
� � Reformulation: We would like to suggest that implementation of our proposed changes, which
would be highly welcomed by students and outside firms, would ensure a more effective curriculum to prepare students for their future.
The�students�writing�needs� to�undergo� several�pedagogic�processing� stages� for�reformulation�into�a�more�professional�type�of�discourse.�First,�students�need�to�be�reminded�of� the�given�and�new�paradigm�operating� in�this�extract.� In�their�context�of�writing,�these�proposed�changes�‘are�highly�welcome�for�students�and�outside�firms’�refers�to�information�already�mentioned�in�a�previous�sub-section,�while�the�Evaluation�element�of�the�proposed�changes�is�new�information�for�the�reader.�Thus,�it�would�be�most�appropriate�to�assign�the�information�previously�mentioned�to�a�relative�clause.�Then,�Just The Word,�a�collocations�search�engine�which�provides�an�interface�for�the�BNC,�was�used�for�a�search�on�implementa-tion.�This�gave�the�results�shown�in�Figure�9-1�below.
However,� the� collocations� for� implementation� only� provide� ‘frames’� so� the�teacher�would�have�to�sensitise�students�to�‘authenticating’�the�corpus�data�to�fit�their�writing�environments�for�it�to�be�appropriate�from�both�a�lexico-grammati-cal�and�pragmalinguistic�point�of�view.�First,�students�have�to�be�alerted�to�the�fact�that�when�‘implementation’�collocates�with�‘lead�to’�or�‘result�in’,�these�verbs�tend�to�be�followed�by�something�negative,�whereas�‘provide’�and�‘ensure’�usually�have�a�positive�semantic�prosody.�(This�type�of�information�can�be�gleaned�by�looking�at� the� concordance� output.)� Second,� concordance� samples� have� to� be� changed�
136� Corpus-based�Analyses�of�Problem-Solution�Pattern�
into�‘examples’�to�fit�the�students’�writing�environment�through�mitigation�mark-ers�such�as�‘we�would�like�to�suggest...’�and�the�use�of�‘would’�instead�of�‘will’.
The�same�type�of�search�could�be�conducted�for�‘problem’,�which�would�throw�up�those�two-way�signalling�verbs,�e.g.�alleviate, minimize, relieve,�for�linking�the�Problem�and�Solution�elements,�which�were�found�to�be�lacking�in�student�writ-ing.�But�again,�the�concordance�output�would�probably�have�to�be�‘authenticated’�for�the�writing�context.
Milton�(2006:�125)�mentions� that�such�kind�of�programs�as� the�one�shown�above�‘would�turn�the�notion�of�appropriation�around�and�point�learners�to�re-sources�where,�as�in�Bakhtin’s�(1981)�concept,�they�would�be�the�ones�appropriat-ing�the�usage�of�more�experienced�writers�of�the�L2’.�The�example�of�implementa-tion�shows�that�students�need�to�both�‘appropriate’�and�‘authenticate’�the�corpus�data�for�their�own�writing�purposes.�
While�examining�the�collocations�shown�in�Figure�9-1,�students�could�be�en-couraged�to�browse�the�other�collocations�on�their�own,�in�the�spirit�of�Bernar-dini’s� (2002,�2004)�philosophy�of� ‘The� learner�as� traveller’,� alighting�on�aspects�which�were�of�potential�interest�to�them�in�a�type�of�discovery�or�serendipitous�learning.�Although�this�type�of�incidentalist�learning�has�been�criticized�by�Swales�(see�Swales�2004;�Lee�&�Swales�2006)�and�may�be�rather�time-consuming�and�lead�students�down�a�few�blind�alleys,�some�exploratory�work�shows�that�this�trial-and-error�approach�does�seem�to�have�a��motivational�appeal�(Flowerdew�2008c).�
Another�consideration�concerns�the�interpretation�of�the�corpus�data�by�stu-dents,�an�issue�which�does�not�seem�to�have�been�discussed�much�in�the�litera-
Figure 9-1. Collocation�search�for�‘implementation’�in�Just The Word
� Chapter�9.� General�conclusions�and�implications�for�pedagogy� 137
ture�on�pedagogic�applications.�As�Hunston�(2002:�65)�notes�‘Concordance�lines�present�information;�they�do�not�interpret�it.�Interpretation�requires�the�insight�and�intuition�of�the�observer’.�The�‘interpretation’�of�the�data�by�the�analyst�can�be�seen�as�paralleling�the�inductive�learning�approach�commonly�associated�with�DDL�in�which�learners�extrapolate�rules�from�their�‘readings’�of�the�concordance�lines.�Carter�and�McCarthy�(1995)�expand�on�this�exploratory�approach,�term-ing�it�the�‘three�Is’�(illustration-interaction-induction),�with�‘illustration’�meaning�examining�corpus�data�and�‘interacting’�discussing�and�exchanging�opinions�on�the�data.
With� this� consideration� in� mind,� I� would� like� to� examine� ergative� verbs,�which�have�been�shown�to�be�another�area�of�difficulty�for�students.�In�the�sen-tence�below�‘with’�can�be�seen�as�a�causative�marker,�followed�by�the�effect�in�the�second�part�of�the�sentence.
With a very crowded schedule, students’ level of motivation was decreased.
Those�change-of-state�verbs�such�as�‘decrease’,�‘increase’�and�‘develop’,�which�have�three� possible� voices� (active;� passive;� middle� (or� ergative),� pose� particular� dif-ficulty�for�students�both�in�terms�of�grammatical�complexity�and�search�strate-gies.�As�noted�in�Chapter�7,�Celce-Murcia�(2002:�147)�notes�that�advanced�level�students� tend� to�overpassivise�such�verbs,�using� the�passive� in�cases�where� the�ergative�should�be�used,�e.g.�Over the period of the study the learners’ VOT values for … were decreased,�a�similar�misuse�to�the�example�given�from�STUCORP.
However,�students�have�been�found�to�have�difficulties�in�working�out�induc-tively�the�various�usage�patterns�of�such�ergative�verbs�from�truncated�concor-dance�lines�in�a�corpus�of�reports.�Prompting�was�required�from�the�class�teacher�to�encourage�students�to�look�at�the�wider�context�in�order�to�notice�that�it�was�the�subject�that�determined�the�voice,�thus�following�Sinclair’s�principle�of�language�viewed�as�‘extended�units�of�meaning’.�In�this�regard,�Celce-Murcia’s�observes�that�‘With� the�verbs� increase� and�decrease [the�ergative]� tends� to�be�used�when� the�inanimate�subject� is�objectively�or�subjectively�measurable�(rather�than�an�ani-mate�agent/dynamic�instrument�subject)�−�both�of�which�favor�active�voice�−�or�a�patient�subject�−�for�the�passive�voice’�(p.�146).�With�oral�prompting�from�the�teacher,� students� were� able� to� articulate,� in� their� own� words,� this� phraseologi-cal�tendency,�by�examining�selected�concordance�output�generated�by�the�Word�Neighbours�concordancing�software�shown�in�Figure�9-2�(see�Milton�2006;�Flow-erdew�2008c).
The�above�example�shows�that�a�purely�inductive�approach�to�interpretation�of�corpus�data�may�sometimes�be� too�sophisticated� for�students.�Although�de-ductive�vs.�inductive�approaches�are�usually�presented�as�diametrically�opposed,�
138� Corpus-based�Analyses�of�Problem-Solution�Pattern�
I�would�like�to�suggest�that�rather,�they�could�be�seen�on�a�cline,�with�the�teacher�providing�hints�on�the�rule�in�cases�where�students�did�not�have�enough�knowl-edge�to�interpret�the�concordance�lines�by�themselves.�While�Flowerdew�(2008c)�reports�discussion�activities�where�hints�are�given�orally,�Milton�(2004,�2006)�de-scribes�a�writing�tutor�program�where�various�types�of�clues�(grammatical,�lexical,�links�to�suitable�corpora)�can�be�inserted�by�the�tutor.�Thus,�it�may�be�necessary�for� some�kind�of� intermediary� stage� to� facilitate� interpretation�of� concordance�output�when�a�more�inductive�approach�is�used.�
This�discussion�of�some�critical�points�in�DDL�has�highlighted�the�fact�that�raw�corpus�data�may�have�to�undergo�a�‘pedagogic�processing’�stage�with�some�kind�of�intervention�on�the�part�of�the�teacher.�As�I�have�suggested,�this�may�in-volve�‘authenticating’�the�data�to�fit�the�students’�context�of�writing�as�Widdowson�advocates,�or�may�involve�providing�various�types�of�clues,�either�verbal�or�writ-ten,�to�aid�students’�in�the�interpretation�of�concordance�lines.
Overall conclusions
In�the�Festschrift�volume�in�honour�of�John�Sinclair�(Baker,�Francis�&�Tognini-Bonelli� 1993)� and� the� Festschrift� volume� in� honour� of� Michael� Hoey� (Scott� &�Thompson�2001),�even�though�these�volumes�are�published�eight�years�apart,�we�can�still�see�the�predominant�role�that�linguistic�patterns�play�in�the�disentangling�of�corpus�data.�If�we�consider�patterns�/�patternings�as�a�‘headword’�subsuming�‘entries’�such�as�phraseology,�collocation�and�colligation,�we�can�see�this�notion�is�like�a�leitmotif�running�through�many�articles�in�both�these�volumes�(cf.�Francis�1993;�Hunston�2001).�
You�can�select�a�word/phrase�and�right-click�to�get�definitions,�pronunciation,�translations,�similar�words,�more�contexts,�etc.
Search results for has decreased by (VERB VERB PREP) Text type
1 In�spite�of�this,�coniferous�output�has�decreased�by�nearly�8�million�m3�and�non-coniferous�output�by�nearly�10�million�m3�…more
Reports�ir-97-099.txt
2 On�the�average�the�number�of�iterations�needed�to�solve�the�prob-lems�has�decreased�by�1:62%�and�the�average�time�was�cut�by�8.33%�…more
Reports�WP-95-113.txt
Figure 9-2. Context�search�for�‘has�decreased’�in�Word Neighbours
� Chapter�9.� General�conclusions�and�implications�for�pedagogy� 139
This�book�has�explored�some�of�the�central�notions�of�patterns�expounded�in�these�volumes�with�specific�reference�to�the�Problem-Solution�pattern�in�a�pro-fessional�and�apprentice�corpus.�Moreover,�an�attempt�has�been�made�to�exam-ine� the� lexico-grammatical� patterning� of� the� Problem-Solution� pattern� from� a�more�textlinguistic�perspective,�outlined�in�Chapter�2.�Categories�from�systemic�functional�grammar,�such�as�Inscribed�and�Evoking�lexis,�grammatical�metaphor�nouns�and�Theme/Rheme�patterning,�have�been�a�useful�aid�for�analysis�of�the�data.�At� the�same�time,� recurring�patterns�have�also�provided�evidence� for� the�discursive�practices�of�the�particular�genres�under�investigation.�Comparisons�of�certain�features�in�STUCORP�with�PROFCORP�and�the�BNC�have�revealed�key�areas�where�students’�deficiencies�in�writing�lie.�It�is�hoped�that�this�corpus-based�lexico-grammatical�analysis�of�the�Problem-Solution�pattern�in�two�corpora�has�made�a�contribution�to�this�ever-developing�field�and�suggested�some�worthwhile�avenues�for�future�exploration.�
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App
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413
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722
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48.
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146� Corpus-based�Analyses�of�Problem-Solution�Pattern�
(con
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� Appendices� 147
(con
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NB:
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148� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Appendix 3-3. Guidelines for Project
Stages�of�the�Project
Stage 1: Planning a study–� nature�of�an�investigation�–� identifying�a�problem�or�need–� scope�of�an�investigation–� choosing�a�topic
Stage 2: Methods of data collectionPrimary�sources–� making�observations–� interviewing–� designing�a�survey�questionnaire
Secondary�sources�Stage 3: Writing a report–� documenting�activities�(writing�a�progress�report�on�data�collection)–� discussing�data�(reporting�and�interpreting�data)–� types�of�reports�(information,�feasibility,�recommendation)�
Scope�of�an�Investigation
When�you�are�planning�your�research�project,�there�are�many�questions�that�you�may�need�to�consider.�These�may�include:
1.� Is�there�a�problem?�What�evidence�do�I�have�that�this�problem�exists?�How�serious�is�the�problem?
2.� What�possible�solutions�are�there�to�an�existing�problem?�Are�these�solutions�technically,�economically�and�socially�feasible?
3.� Is�there�a�need�for�a�new�development?�What�is�the�evidence�that�this�need�exists?4.� How�can�an�existing�need�be�met?�Can�it�be�met�in�a�way�that�is�technically,�economically�
and�socially�feasible?
Obviously,� the�majority�of� research�projects�will�not�attempt� to�consider�all� these�questions�fully.�Instead,�projects�tend�to�concentrate�on�answering�one�or�two�questions.
� Appendices� 149
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� Appendices� 151
(con
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� Appendices� 153
Appendix 4-2. Keyword list in a PROFCORP report
1_ecI008.kws (keyness)
N Word Freq. ECL.txt
% Freq. Corpus % Key-ness
P
1 SLAUGHTERHOUSE 21 1.52 � � 1 269.2 00000000000012 IMPACTS 16 1.15 � � 5 188.2 0.0000000000013 MITIGATION 12 0.87 � � 0 158.4 0.0000000000014 ENVIRONMENTAL 18 1.30 � 54 156.9 0.0000000000015 MEASURES 13 0.94 � 83 � 95.7 0.0000000000016 WASTEWATER � 7 0.51 � � 0 � 92.4 0.0000000000017 BPP � 7 0.51 � � 0 � 92.4 0.0000000000018 NOISE 12 0.87 � 78 � 87.9 0.0000000000019 STUDY 15 1.08 216 0.02 � 87.6 0.00000000000110 OPERATIONS 11 0.79 � 68 � 81.6 0.00000000000111 WASTES � 7 0.51 � � 3 � 80.2 0.00000000000112 SEIA � 6 0.43 � � 0 � 79.2 0.00000000000113 GUIDELINES � 8 0.58 � 13 � 77.7 0.00000000000114 PROPOSED 11 0.79 100 � 73.8 0.00000000000115 EPD � 5 0.36 � � 0 � 66.0 0.00000000000116 WILL 29 2.09 � � 3,107 0.31 � 62.2 0.00000000000117 RECOMMENDED � 7 0.51 � 22 � 60.4 0.00000000000118 MANAGEMENT 10 0.72 139 0.01 � 59.0 0.00000000000119 ODOUR � 5 0.38 � � 2 � 57.6 0.00000000000120 EMS � 5 0.38 � � 2 � 57.6 0.00000000000121 WASTE � 7 0.51 � 37 � 53.9 0.00000000000122 IMPACT � 9 0.65 126 0.01 � 53.0 0.00000000000123 BLOOD � 8 0.58 � 81 � 52.0 0.00000000000124 TREATMENT � 8 0.56 � 85 � 51.3 0.00000000000125 ENSURE � 9 0.65 165 0.02 � 48.3 0.00000000000126 AREA 11 0.79 370 0.04 � 46.5 0.00000000000127 CONSTRUCTION � 8 0.58 119 0.01 � 46.2 0.00000000000128 SURROUNDING � 6 0.43 � 39 � 43.9 0.000000000001
154� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Appendix 4-3. Keyword list in a STUCORP report
1 cs0006.kws (keyness)
N Word Freo. 1_CS.txt Freq. Corpus % Keyness P
1 COMPUTER 53 2.84 281 0.03 377.9 0.0000000000012 BARNS 29 1.55 � � 4 341.4 0.0000000000013 COST 22 1.16 � � 0 277.4 0.0000000000014 STUDENTS 35 1.87 326 0.03 212.9 0.0000000000015 PRINTERS 13 0.70 � � 3 148.4 0.0000000000016 SOFTWARE 17 0.91 � 66 130.4 0.0000000000017 COMPUTERS 16 0.66 � 48 129.9 0.0000000000018 PACKAGES 12 0.64 � � 8 124.3 0.0000000000019 TONER � 8 0.43 � � 0 100.8 0.00000000000110 FACILITIES 16 0.86 132 0.01 100.8 0.00000000000111 HKUST � 7 0.37 � � 0 � 88.2 0.00000000000112 DIAL-1N � 6 0.32 � � 0 � 75.6 0.00000000000113 STUDENT 10 0.54 � 52 � 71.4 0.00000000000114 SERVER � 7 0.37 � � 7 � 68.6 0.00000000000115 HARDWARE � 7 0.37 � 22 � 56.2 0.00000000000116 PROBLEMS 12 0.64 244 0.02 � 55.3 0.00000000000117 COST 12 0.64 247 0.02 � 55.0 0.00000000000118 UTILIZATION � 5 0.27 � � 3 � 52.4 0.00000000000119 ‘INSUFFICIENT � 6 0.32 � 15 � 50.5 0.00000000000120 NEED 14 0.75 527 0.05 � 48.4 0.00000000000121 FEASIBILITY � 5 0.27 � � 6 � 47.9 0.00000000000122 SERVERS � 5 0.27 � � 6 � 47.9 0.00000000000123 TRAYS � 5 0.27 � � 7 � 46.7 0.00000000000124 INADEQUATE � 6 0.32 � 23 � 46.1 0.00000000000125 HELPERS � 4 0.21 � � 1 � 45.4 0.00000000000126 TELEPHONE � 7 0.37 � 6 � 43.3 0.000000000001
� Appendices� 155
Appendix 4-4. Key Technical Vocabulary in PROFCORP
A. Environmental Study
EIA� � � (28)�� Environmental�Impact�AssessmentIEIA� � (2)� � Initial�Environmental�Impact�AssessmentDEIA� � (2)� � Detailed�Environmental�Impact�AssessmentSEIA� � (2)� � Supplementary�Environmental�Impact�AssessmentCEIA� � � � Conceptual�Environmental�Impact�AssessmentIAR�� � � � Initial�Assessment�ReportEDS�� � � � Expanded�Development�StudyEIS� � � � � Environmental�Impact�StudyEISA� � � � Environmental�Impact�and�Safety�Assessment
B. Environmental rules and regulations
EMA� � (6)� � Environmental�Monitoring�and�AuditHKPSG� � (4)� � Hong�Kong�Planning�and�Standards�GuidelinesAQO� � (2)� � Air�Quality�ObjectivesASR�� � (2)� � Area�Sensitivity�RatingLWCS� � (2)� � Livestock�Waste�Control�SchemeWPCO� � (2)� � Water�Pollution�Control�OrdinanceANL� � � � Allowable�Noise�LevelsNCO� � � � Noise�Control�OrdinanceTMP� � � � Turfgrass�Management�PlanEMS� � � � Environmental�Management�SystemWQOs� � � � Water�Quality�ObjectivesEMP� � � � Environmental�Monitoring�PlanSMP� � � � Sewerage�Master�Plan
C. Areas/zones
SIA� � � (2)� � Special�Industries�Area�MBAs� � � � Marine�Borrow�AreasCWA� � � � Cargo�Working�AreaCBAs� � � � Container�Back-up�AreasGIA�� � � � General�Industries�AreaCDA� � � � Comprehensive�Development�AreaMCZs� � � � Mariculture�ZonesFCZs� � � � Fish�Culture�ZonesSSSI�� � � � Site�of�Special�Scientific�InterestCMPs� � � � Contaminated�Mud�Pits
D. Installations
NSR(s)� � (9)� � Noise�Sensitive�Receiver(s)ASR(s)� � (2)� � Air�Sensitive�ReceiversPHIs� � � � Potentially�Hazardous�InstallationsSSR� � � � � Secondary�Surveillance�RadarSBC�� � � � Sub-Marine�Power�Cable
156� Corpus-based�Analyses�of�Problem-Solution�Pattern�
LTPs� � � � Large�Thermal�Power�StationGRS�� � � � Gas�Receiving�Station
E. Gases / Metals causing environmental damage
TSP�� � (7)� � Total�Suspended�ParticulatesRSP�� � (3)� � Respirable�Suspended�ParticulatesLFG�� � (3)� � Landfill�GasCFCs� � � � ChlorofluorocarbonsSS� � � � � Suspended�Solids
Leachate�� (6)� � ferricHydrogen�sulphide� copperCarbon�dioxide� � zincChloride�� � � TBT�� TributyltinOxides� � � � LPG�� Liquefied�Petroleum�GasNitrogen�� � � methanogenicOzone� � � � nitrousHalons� � � � AmmoniaMethane
F. Mitigation Measures
LTF�� � � � Leachate�Treatment�Facility�CIF� � � � � Centralised�Incineration�FacilityRTFs� � � � Refuse�Transfer�FacilitiesLRWF� � � � Low-level�Radioactive�Waste�Storage�FacilitySTW� � � � Sewage�Treatment�WorksVFPW� � � � Village�Flood�Protection�WorksMDC� � � � Main�Drainage�Channel�(works)BPP�� � � � By-Product�PlantPPVL� � � � Pillar�Point�Valley�LandfillKTIPS� � � � Kwun�Tong�Intermediate�Pumping�StationGRIPD� � � � Green�Island�Reclamation�Public�Dump
G. Measurements
DBa�� � (8)� � Decibel�scaleHa� � � (3)� � hectares�Ug/m3� � (3)� � (measure�of�TSPs�in�air)�Cu� � � � � cubic�metres��Mgl�� � � � milligramVph�� � � � vehicles�per�hourKg.Km.
H. Technical Vocabulary (misc.)
Bund� � (2)� � soil�wall�built�across�slope�to�retain�waterSousa� � (2)� � type�of�dolphin�(white)Cantilever� � � type�of�barrier
� Appendices� 157
Penaeid� � � � type�of�shrimpBenthic� � � � living�on�the�floor�of�the�seaPhytoplankton� � microscopic�plants�which�float�in�the�seaGrab-dredged�� (2)� Stormwater� � (5)Trailer-dredged� � Groundwater� � (4)WashwaterCofferdamFung�Shui
Appendix 4-5. Key Technical Vocabulary in STUCORP
Computer-related
Internet� � (7)� � encryption�Modem(s)� (5)� � e-mailDial-in� � (3)� � AspenDial-up� � (2)� � SybaseDot-matrix� (3)� � SupernetServer(s)�� (3)� � SunSparc� � � � � Visual�Basic
PCs�� � (2)MS_DosISPsPPP�(fast�modem�pool)
University Departments / Service Centres
UST�� � (22)�� University�of�Science�and�Technology�HKUST� � (50)�� Hong�Kong�University�of�Science�and�Technology�CCST� � (16)�� Centre�for�Computing�Services�and�TelecommunicationsSAO�� � (8)� � Student�Affairs�OfficeARR� � (5)� � Admissions,�Registration�and�Records�OfficeSHO� � (2)� � Student�Housing�OfficeSEPO� � (2)� � Safety�and�Environmental�Protection�OfficeEMO� � (2)� � Estates�Management�OfficeCS� � � (6)� � Computer�ScienceCPEG� � (2)� � Computer�EngineeringEEE�� � � � Electrical�and�Electronic�EngineeringSAC�� � (2)� � Self-Access�CentreSU� � � � � Students’�UnionHSS�� � � � Humanities�and�Social�Science
158� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Appendix 4-6. List of key-key words in PROFOCORP
N Word Of 60 As %
1 IMPACTS 50 83.332 CONSTRUCTION 47 78.333 NOISE 44 73.334 MITIGATION 43 71.675 ENVIRONMENTAL 43 71.676 QUALITY 39 65.007 SITE 34 56.678 AREA 32 53.339 WILL 30 50.0010 MEASURES 30 50.0011 MONITORING 29 48.3312 EIA 28 46.6713 PROPOSED 28 46.6714 WATER 27 45.0015 HONGOKONG 27 45.0016 RECOMMENDED 27 45.0017 MARINE 27 45.0018 IMPACT 26 43.3319 BE 26 43.3320 RECEIVERS 24 40.0021 ASSESSMENT 23 38.3322 TRAFFIC 23 38.3323 DREDGING 22 36.6724 LEVELS 22 36.6725 SENSITIVE 22 36.6726 WORKS 21 35.0027 ROAD 20 33.3328 RECLAMATION 20 33.3329 WASTE 20 33.3330 DUST 20 33.3331 VISUAL 15 25.0032 SEDIMENT 15 25.0033 THE 15 25.0034 CONTAMINATED 14 23.3335 DISPOSAL 14 23.3336 AIR 14 23.3337 STUDY 14 23.3338 EPD 14 23.3339 EXISTING 13 21.67
� Appendices� 159
(continued)
40 DESIGN 12 20.0041 SEWAGE 12 20.0342 BAY 12 20.0043 CONTRACTOR 11 18.3344 PHASE 11 18.3345 LOCATED 11 18.3346 TUENOMUN 10 16.6747 DEVELOPMENT 10 16.6748 OPERATIONAL 10 16.6749 POLLUTION 10 16.6750 EMISSIONS 10 16.6751 SEDIMENTS 10 16.6752 LANDFILL 10 16.6753 OPERATION 10 16.6754 POTENTIAL 10 16.6755 ODOUR � 9 15.0056 DURING � 9 15.0057 TREATMENT � 9 15.0058 LANDAU � 9 15.0059 RESIDENTIAL � 9 15.0060 AREAS � 8 13.3361 REQUIREMENTS � 8 13.3362 LANDSCAPE � 8 13.3363 RECOMMENDATION � 8 13.3364 PROJECT � 8 13.3365 ISLAND � 8 13.3366 ADJACENT � 8 13.3367 FACILITIES � 8 13.3368 ACTIVITIES � 8 13.3369 DBA � 8 13.3370 PREDICTED � 8 13.3371 OPERATIONS � 7 13.3372 ECOLOGICAL � 7 13.3373 HARBOUR � 7 13.3374 TSP � 7 13.3375 LEACHABLE � 6 13.3376 ALIGNMENT � 6 13.3377 DETAILED � 6 10.0078 IMPLEMENTATION � 6 10.0079 NSRS � 6 10.0380 DREDGED � 6 10.0081 STANDARDS � 6 10.0082 CONTRACT � 6 10.0083 EMA � 6 10.00
160� Corpus-based�Analyses�of�Problem-Solution�Pattern�
(continued)
84 SEWERAGE � 6 10.0085 GAS � 6 10.0086 TSINGOYI � 6 10.0087 EFFLUENT � 6 10.0088 MATERIAL � 6 10.0089 STORMWATER � 5 � 8.3390 CONTAINER � 5 � 8.3391 TSEUNGOKWANOO � 5 � 6.3392 DISCHARGE � 5 � 8.3393 ROADS � 5 � 8.3394 DISCHARGES � 5 � 8.3395 CHANNEL � 5 � 8.3396 FACILITY � 5 � 8.3397 ROUTE � 5 � 8.3398 WASTES � 5 � 8.3399 OPTIONS � 5 � 8.33100 AUDIT � 5 � 8.33101 MINIMISE � 5 � 8.33102 PLAN � 5 � 8.33103 PORTAL � 5 � 8.33104 PORT � 5 � 8.33105 OXYGEN � 5 � 8.33106 DEDGERS � 5 � 8.33107 CONCENTRATIONS � 5 � 8.33108 BARRIERS � 5 � 8.33109 WOULD � 5 � 8.33110 PONDS � 5 � 8.33111 PLANT � 5 � 8.33112 BLASTING � 5 � 8.33113 SCHEME � 5 � 8.33114 DISCOVERY � 5 � 8.33115 DRAINAGE � 5 � 8.33116 RIVER � 5 � 6.67117 CONTAMINATION � 4 � 6.67116 TERMINAL � 4 � 6.67119 JETTY � 4 � 6.67120 COMPLIANCE � 4 � 6.67121 RESTORATION � 4 � 6.67122 GROUNDWATER � 4 � 6.67123 MANAGEMENT � 4 � 6.67124 ECOLOGY � 4 � 6.67125 REDUCE � 4 � 6.67126 SEAWALL � 4 � 6.67127 PUMPING � 4 � 6.67
� Appendices� 161
(continued)
128 FISH � 4 � 6.67129 FILL � 4 � 6.67130 ENSURE � 4 � 6.67131 FISHERIES � 4 � 6.67132 SILT � 4 � 6.67133 SUSPENDED � 4 � 6.67134 STREAM � 4 � 6.67135 CONTROL � 4 � 6.67136 MODELLING � 4 � 6.67137 INDIRECT � 4 � 6.67138 METALS � 4 � 6.67139 HKPSG � 4 � 6.67140 SPOIL � 4 � 6.67141 SHOULD � 4 � 6.67142 SCENARIOS � 4 � 6.67143 KOWLOON � 4 � 6.67144 ASSOCIATED � 4 � 6.67145 APPROXIMATELY � 4 � 6.67146 PFNGOCHAU � 4 � 6.67147 VEGETATION � 4 � 6.67148 YUENOLONG � 4 � 6.67149 WATERS � 4 � 6.67150 PHASES � 4 � 6.67151 ORDINANCE � 4 � 6.67152 VICTORIA � 4 � 6.67153 TUNNEL � 4 � 6.67
162� Corpus-based�Analyses�of�Problem-Solution�Pattern�
Appendix 4-7. List of key-key words in STUCORP
N Word Of 80 As %
1 STUDENTS 69 86.252 RESPONDENTS 51 63.753 HKUST 50 62.504 QUESTIONNAIRE 33 41.255 STUDENT 24 30.006 UST 22 27.507 QUESTIONNAIRES 19 23.758 INTERVIEW 17 21.259 THE 17 21.2510 CCST 16 20.0011 COURSES 15 18.7512 SURVEY 14 17.5013 DATA 14 17.5014 USERS 13 16.2515 WE 13 16.2516 COURSE 12 15.0017 HONGOKONG 12 15.0018 UNDERGRADUATE 12 15.0019 COMPUTER 11 13.7520 FIGURE 11 13.7521 CAN 11 13.7522 SEMESTER 10 12.5323 HALL 10 12.5024 CAMPUS 10 12.5025 STAFF 10 12.5026 USE 10 12.5027 SYSTEM 10 12.5028 FACILITIES 10 12.5029 OPINIONS 10 12.5030 SPORTS � 9 11.2531 BARNS � 9 11.2532 OBSERVATION � 9 11.2533 UNIVERSITY � 9 11.2534 SERVICE � 9 11.2535 LECTURERS � 9 11.2536 INTERVIEWS � 8 10.0037 PROBLEM � 8 10.0038 SAO � 8 10.0039 FEASIBILITY � 0 10.00
� Appendices� 163
(continued)
40 STUDY � 8 10.0041 SITUATION � 8 10.0042 SERVICES � 8 10.0043 BARN � 7 � 8.7544 PRINTERS � 7 � 8.7545 RESIDENTS � 7 � 8.7546 QUOTA � 7 � 8.7547 LIBRARY � 7 � 8.7548 COMPUTERS � 7 � 8.7549 INTERNET � 7 � 8.7550 PROVIDED � 7 � 8.7551 REGISTRATION � 7 � 8.7552 ACADEMIC � 7 � 8.7553 SECURITY � 6 � 7.5054 THEIR � 6 � 7.5055 CS � 6 � 7.5056 PRINTING � 6 � 7.5057 WERE � 6 � 7.5058 DEPARTMENT � 6 � 7.5059 FEE � 6 � 7.5060 FEASIBLE � 6 � 7.5061 USUAGE � 6 � 6.2562 CARD � 6 � 6.2563 MATERIALS � 5 � 6.2564 HELPERS � 5 � 6.2565 HOURS � 5 � 6.2566 MACHINES � 5 � 6.2567 BOOKING � 5 � 6.2568 OUR � 5 � 6.2569 TIME � 5 � 6.2570 FOOD � 5 � 6.2571 ARR � 5 � 6.2572 UTILIZATION � 5 � 6.2573 HOMEWORK � 5 � 6.2574 THEY � 5 � 6.2575 SCIENCE � 5 � 6.2576 USING � 5 � 6.2577 SCHOOL � 5 � 6.2578 RESOURCES � 5 � 6.2579 SHOP � 5 � 6.2580 INSUFFICIENT � 5 � 6.2581 RECOMMENDATIONS � 5 � 6.2582 IS� � 5 � 6.2583 PRINTER � 5 � 6.25
164� Corpus-based�Analyses�of�Problem-Solution�Pattern�
(continued)
84 LANGUAGE � 5 � 6.2585 ASSISNMENTS � 5 � 6.2586 TERTIARY � 4 � 6.2587 QUESTIONS � 4 � 5.0088 CENTER � 4 � 5.0089 CENTRE � 4 � 5.0090 RANDOM � 4 � 5.0091 COPY � 4 � 5.0092 SOLUTIONS � 4 � 5.0093 PHOTOCOPYING � 4 � 5.0094 LABORATORY � 4 � 5.0095 PERIOD � 4 � 5.0096 LEARNING � 4 � 5.0097 SOLUTION � 4 � 5.0098 JOB � 4 � 5.0099 THAT � 4 � 5.00100 EMAIL � 4 � 5.00101 PASSWORD � 4 � 5.00102 PRICE � 4 � 5.00103 SPORT � 4 � 5.00104 CANTEEN � 4 � 5.00105 DEPRTMENTS � 4 � 5.00106 MONEY � 4 � 5.00107 HK � 4 � 5.00108 UG � 4 � 5.00109 PROBLEMS � 4 � 5.00110 BELONGINGS � 4 � 5.00111 UNDERGRADUATES � 4 � 5.00112 HALLS � 4 � 5.00113 STOLEN � 4 � 5.00114 USED � 4 � 5.00115 MOST � 4 � 5.00116 NEED � 4 � 5.00117 PUTONGHUA � 4 � 5.00118 CARDS � 4 � 5.00
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AÄdel,�A.� 102Aston,�G.� 53
BBaker,�M.� 37Baker,�P.� 13,�18Barlow,�M.� 97Belcher,�D.� 133Bernardini,�S.� 136Bhatia,�V.�K.� 14,�15,�16,�18Biber,�D.� 13,�25Blommaert,�J.� 18Bowker,�L.� 43Burnard,�L.� 53
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DDe�Beaugrande,�R.� 133de�Haan,�P.� 24Devitt,�A.� 88�
FFairclough,�N.� 18Fang,�X.� 58Firth,�J.�R.� 8Flowerdew,�J.� 7,�10,�24,�Flowerdew,�L.� 3,�10,�35,�54,�69,�
130,�136Francis,�G.� 7,�8,�105,�106Fries,�P.� 85,�86
GGavioli,�L.� 90,�134Gisborne,�N.� 107Gledhill,�C,� 91,�124Goodman,�A.� 43
Grabe,�W.� 14,�15Granger,�S.� 97Green,�C.� 111,�112
HHalliday,�M.�A.�K.� 17,�20,�39,�
54,�70,�87,�127Harris,�J.� 6Hasan,�R.� 54,�70Hinkel,�E.� 109,�127Hoey,�M.� 1,�2,�34,��39,�48,�58,�
105,�138Howarth,�P.� 10Hunston,�S.� 15,�34,�55,�88,�137Hyland,�K.� 19,�68,�102
IIvanič,�R.� 7
JJohansson,�S.� 40Jordan,�R.� 5,�6,�7
KKaplan,�W.� 14,�15Kennedy,�G.� 25,�27,�58Kifle,�N.� 132Knowles,�G.� 31
LLee,�D.� 23Leech,�G.� 26Lin,�L.� 104,�111Lorenz,�G.� 102Louw,�B.� 32,�58
MMarco,�M.�J.�L.� 54Martin,�J.�R.� 33,�34,�35Mauranen,�A.� 40McCarthy,�M.� 2,�5,�134,�137McEnery,�T.� 13,�14,�25,�132Meunier,�F.� 134,�135Meyer,�C.� 25,�26
Milton,�J.� 97,�102,�136,�138Mohan,�B.� 135Moon,�R.� 65Mudraya,�O.� 40Mukherjee,�J.� 134
NNesselhauf,�N.� 97,�134
OO’Halloran,�K.� 17,�18
PPartington,�A.� 14,�34Payne,�E.� 43Pearson,�J.� 43Pravec,�R.� 97Proctor,�M.� 4,�5,�8,�34
RRenouf,�A.� 29,�31,�71
SSchmidt,�H.-J.� 7,�104,�107Scott,�M.� 8,�39,�40,�105Seidlhofer,�B.� 40Sinclair,�J.�McH.� 10,�24,�25,�31,�
32,�55,�65,�134,�137,�138Stubbs,�M.� 10,�16,�55,�56,�68Swales,�J.� 14,�53,�136
TTognini-Bonelli,�E.� 18,�26,�
31,�138Tribble,�C.� 15,�18,�40
WWiddowson,�H.� 15,�16,�18,�130,�
134,�135,�138Wilkins,�D.� 76Williams,�G.� 26,�38Winter,�E.� 4,�5,�6
YYang,�H.� 38,�42
Author index
Aabbreviation� 27,�28,�29,�43,�48
see also Latin�abbreviations�27–29
aboutness� 39academic�writing� �54,�97,�127ACRONYM�project� 29,�71adjective� 10,�34,�38,�55,�78,�82,�
83,�90,�93,�104,�108,�118,�119,�120,�121,�123,�125
anaphora� 28,�69,�70�see also anaphoric�noun� 7,�
104,�105,�106,�108anticipatory�‘It’� 111A-Noun� 34,�70,�105,�106,�129Applied�Science�component� �
53,�68,�72,�80,�81,�90,�94,�98,�103,�106,�111,�113,�122,�131
Appraisal�system� 33,�34apprentice�writers� 49,�50,�51,�
57,�98,�131,�132associated with� 68,�69,�74,�94,�
129,�130audience� 21,�23authenticity� 15,�130,�134,�135,�
136,�138
BBank�of�English� 17,�46,�90BNC�(British�National�Corpus)� ��
� 8,�83,�84,�89,�101,�102,�135see also�Applied�Science�
component� 53,�68,�72,�80,�81,�90,�94,�98,�103,�106,�111,�113,�122,�131
core�written�component� 35,�36,�40,�56
Ccataphoric� 70,�78,�102,�104,�105,�
106,�116,�118causation� 60,�61,�62,�63,�64,�71,��
� 73,�84,�94,�99,�105,�107,�110,��� 113,�126,�129,�130
see also cause-consequence� �2,�6,�11
cause-effect� 54,�59,�76cause-reason� 58,�59,�60,�64,�
66,�69,�100,�107,�113�cause� 56,�60,�64,�65,�72,�99,�
100,�107,�113,�121,�132CDA� 17,�18,�19classificatory�framework� 53,�55,�
57,�75,�76,�115clause�relations� 1–6,�54COBUILD� 35,�36,�67coherence� 7,�46,�84,�91,�93,�110cohesion� 54,�85,�86colligation� 8,�9,�32,�56,�65–69,�
79,�86,�111collocation� 8,�9,�10,�32,�38,�39,�
48,�55–60,�65,�69,�88,�103,�107,�120,�121,�124,�135,�138
Concord 32,�38,�46,�47,�49concordance� 11,�14–19,�106,�131,�
135,�137Condition-Consequence� 59,�
64,�83,�103,�conjuncts� 5,�54connector� 4,�8,�36,�105connotation� 32,�34,�35,�43,�49�context� 5,�7,�15,�16,�18,�34,�36,��
� 37,�101see also contextual�features� �
7,�10,�15,�16,�18,�21,�68,�80,�95�121,�130–134
corpus� 8,�12,�14,�15,�20–30,�38,��� 97,�98,�102,�104,�105,�109,��� 112,�113,�127,�128,�130–135see also Bank�of�English� 17,�
46,�90BNC�(British�National�
Corpus)� 8,�83,�84,�89,�101,�102,�135
corpus�compilation� 24corpus,�general� 8,�22,�25,�35,�
36,�39,�40
corpus,�learner� 97,�127,�128,�132,�134
corpus,�reference� 8,�10,�22,�40,�53
corpus,�specialised� 8,�11,�24,�25,�26,�32,�53,�55,�90,�130
International�Corpus�of�Learner�English�(ICLE)� �97
Professional�corpus�(PROFCORP)� 8,�21,�50
Student�corpus�(STUCORP)� �22,�49
critical�discourse�analysis� 17,�18,�19
Ddata-driven�learning� (DDL)�
134–137deictic� 7,�8,�104delexical�verbs� 79,�80delicacy� 25,�55,�74,�78determiner� 69,�105,�107,�119
EEnglish�as�a�lingua�franca� 40epistemic� 56,�102ergative�verbs� 100,�132,�137error,�type�of� 97,�100,�132ethnographic�considerations� �
19,�130,�131,�134evaluation� 2,�33–38,�56,�61,�69,�
71,�76,�78,�80,�81,�92,�94,�94,�95,�103–106,�110,�116,�118,�120,�121,�126,�127,�135
evoking�items� 34,�37,�48,�93,�126
existential�‘there’� 59,�60,�66,�74,�101,�132
Fformulaic� 15,�72,�74,�103,�113,�
125
Subject index
178� Corpus-based�Analyses�of�Problem-Solution�Pattern�
frequency� 8,�10,�15,�25,�33–42,�49,�51,�63,�72,�84,�89,�92,�123
Ggeneral�corpus� 25,�35,�36,�39,�40genre� 10,�13–20,�39,�40,�44,�130Grounds-Conclusion� 54,�57,�
62,�64,�70,�84,�85,�89,�91,�98,�103,�111,�121,�122,�124,�125
Hhedging� 19,�68,�76,�94,�130however� 3,�8,�9,�10,�11,�19hyphenation� 29,�30hyponym� 29,�35,�71,�130
IICE� 40ICLE� 97ICE-HK� 40idiom� 65inductive�approach� 134–138inscribed�items� 34,�81interlanguage� 97,�111,�132interpersonal� 34,�56,�76,�80,�
109,�113,�116,�122,�126,�128,�131interpretation� 7,�10,�14–20,�35,�
37,�130,�136,�137,�138intertextuality� 79,�80,�88,�89,�
92,�95,�123,�130,�131
Kkey� 37,�39,�40–50,�55,�73,�77,�98,�
112,�115,�117,�126,�130,�131keyness� 10,�39,�40,�44,�71key-key�word� 33,�44,�45,�47,�49,�
50,�57,�63,�98,�110,�115key�word� 10,�29,�33,�41,�50,�58,�
66,�71,�75,�77,�83,�86,�91,�93,�94,�95,�113,�115,�131
LLatin�abbreviations� 27–29learner�corpus� 97,�127,�128,�
132,�134lemma� 31,�32,�56,�60,�71,�72,�120lemmatization� 31,�32lexico-grammatical�patterning� �
14,�54,�55,�57,�64,�72,�73,�77,�78,�80,�92,�93,�99,�105,�106,�109,�118,�122,�124,�126,�131,�134
lexis� 6,�7,�35,�39,�40,�42,�43,�46,�47,�48,�49,�86
MMeans-Result� 59,�64,�66,�70,�
98,�103,�108metadiscourse� 7,�102metalanguage� 50,�101,�128,�132minimise� 55,�58,�60,�65,�66,�69,�
70,�73,�77,�83,�94,�101,�109modals� 56,�58,�60,�86,�101,�
102,�109multilayering� 3,�9,�48,�99
Nnative�speaker� 19,�22,�43,�97,�
100,�124non-native�speaker� 21,�22,��
� 109,�113see also NNS� 97
nominal� 54,�61,�62,�68,�69,�75,�76,�77,�80,�87,�92,�95,�106,�110,�115
nominalisation� 17,�88,�122,�123notional� 54,�76,�130nouns� 36,�38,�90,�95
see also A-nouns� 34,�70,�105,�106,�129,�130,�132
grammatical�metaphor�nouns�94,�95,�122,�124
multi-word�nouns� 27shell�nouns� 7,�105,�107signaling�nouns� 7,�105
Oopen�class�vs.�closed�class� 67overpassivise� 100,�137
Ppassive� 18,�65,�70,�77,�78,�79,�
80,�82,�84,�86,�87,�88,�89,�90,�91,�93,�95,�99,�100,�116,�117,�121,�122,�124,�125,�128,�131,�133,�137
pedagogy� 129,�133–138phraseology� 8,�36,�55,�56,75,�
133,�138pollution� 10,�11,�19,�35,�46,�55polysemy� 65postmodification� 61,�86,�88,�
89,�101,�125
premodification� 60,�61,�64,�69,�71,�81,�83,�90,�93,�104,�105,�106,�109,�120,�121,�123,�125
preposition� 58,�64,�61,�67,�69,�70,�86,�93,�94,�95,�99,�101,�107,�125,�129,�133
prosody� 16,�32,�34,�37,�43,�55,�58,�68,�94,�99,�100,�135
RReason-Result� 57,�58,�60,�64,�
69,�70,�72,�73,�84,�94,�95,�98,�99,�107,�109,�111,�129
reduce� 11,�45,�60,�65,�66,�69,�70,�73,�77,�83,�94,�101
reference�corpus� 8,�10,�22,�40,�53
register� 7,�101,�128,�131,�132repetition� 18,�40,�63,�118representativeness� 21,�24,�25,�
26,�32,�33retrospective� 7,�104,�108Rheme� 8,�57,�58,�61,�66,�67,�69,�
70,�76,�79,�85,�86,�88,�101,�102,�119,�120,�124,�139
rhetorical�13,�15,�51,�86,�89,�103,��� 129,�130see also New�Rhetoric� 19,�88
Sscheme� 46,�70semantic�prosody� 16,�34,�37,�48,�
55,�58,�94,�99,�100,�130,�134semantic�relations� 4,�53,�54,�
76,�107sentence�boundary� 102,�105shell�nouns� 7,�105,�107signaling�nouns� 7,�105size�of�corpus� 21,�23,�24,�25,�26,�
30,�32,�40specialised�corpus� 8,�11,�24,�25,�
26,�32,�53,�55,�90,�130style� 26,�30,�40,�84,�125superordinate� 34,�35,�41,�48,�50,�
71,�75,�90,�93synonym� 2,�6,�32,�46,�65,�71,�
90,�108,�111,�130systemic-functional�grammar� �
17,�20,�33,�130
� Subject�index� 179
Tthematisation� 79,�94,�119Theme� 8,�53,�57,�58,�61,�66,�67,�
69,�70,�76,�79,�85,�86,�88,�91,�102,�119,�120,�124
token� 27,�40type� 27
Uunhedged� 109unidiomatic� 107,�132
Vverbs,�ergative� 100,�132,�137verbs,�delexical� 79,�80verbs,�two-way�signalling� 70,�
73,�132,�136vocabulary�1�&�2�items� 4,�5vocabulary�3�items� 6,�7,�34,�
36,�37vocabulary,�sub-technical� 37–
40,�42,�44,�48,�49,�50,�64,�65
vocabulary,�technical� 38,�39,�42,�43,�45,�61,�63
Wword�boundaries� 27wordlist� 27,�28,�35,�36,�42,�44WordSmith� 30,�38,�46writers,�apprentice� 49,�50,�51,�
57,�98,�131,�132
In the series Studies in Corpus Linguistics (SCL) the following titles have been published thus far or are scheduled for publication:
30 Adolphs, svenja:CorpusandContext.Investigatingpragmaticfunctionsinspokendiscourse.ix,152pp.+index.Expected February 2008
29 Flowerdew, lynne:Corpus-basedAnalysesoftheProblem–SolutionPattern.Aphraseologicalapproach.2008.xi,179pp.
28 BiBer, douglas, Ulla Connor and Thomas A. Upton:DiscourseontheMove.Usingcorpusanalysistodescribediscoursestructure.2007.xii,290pp.
27 sChneider, stefan:ReducedParentheticalClausesasMitigators.AcorpusstudyofspokenFrench,ItalianandSpanish.2007.xiv,237pp.
26 JohAnsson, stig:SeeingthroughMultilingualCorpora.Ontheuseofcorporaincontrastivestudies.2007.xxii,355pp.
25 sinClAir, John Mch. and Anna MAUrAnen:LinearUnitGrammar.Integratingspeechandwriting.2006.xxii,185pp.
24 Ädel, Annelie:MetadiscourseinL1andL2English.2006.x,243pp.23 BiBer, douglas:UniversityLanguage.Acorpus-basedstudyofspokenandwrittenregisters.2006.
viii,261pp.22 sCott, Mike and Christopher triBBle:TextualPatterns.Keywordsandcorpusanalysisinlanguage
education.2006.x,203pp.21 GAvioli, laura:ExploringCorporaforESPLearning.2005.xi,176pp.20 MAhlBerG, Michaela:EnglishGeneralNouns.Acorpustheoreticalapproach.2005.x,206pp.19 toGnini-Bonelli, elena and Gabriella del lUnGo CAMiCiotti (eds.):StrategiesinAcademic
Discourse.2005.xii,212pp.18 röMer, Ute:Progressives,Patterns,Pedagogy.Acorpus-drivenapproachtoEnglishprogressiveforms,
functions,contextsanddidactics.2005.xiv+328pp.17 Aston, Guy, silvia BernArdini and dominic stewArt (eds.):CorporaandLanguageLearners.
2004.vi,312pp.16 Connor, Ulla and Thomas A. Upton (eds.):DiscourseintheProfessions.Perspectivesfromcorpus
linguistics.2004.vi,334pp.15 Cresti, emanuela and Massimo MoneGliA (eds.):C-ORAL-ROM.IntegratedReferenceCorporafor
SpokenRomanceLanguages.2005.xviii,304pp.(incl.DVD).14 nesselhAUF, nadja:CollocationsinaLearnerCorpus.2005.xii,332pp.13 lindqUist, hans and Christian MAir (eds.):CorpusApproachestoGrammaticalizationinEnglish.
2004.xiv,265pp.12 sinClAir, John Mch. (ed.):HowtoUseCorporainLanguageTeaching.2004.viii,308pp.11 BArnBrook, Geoff:DefiningLanguage.Alocalgrammarofdefinitionsentences.2002.xvi,281pp.10 AiJMer, karin:EnglishDiscourseParticles.Evidencefromacorpus.2002.xvi,299pp.9 reppen, randi, susan M. FitzMAUriCe and douglas BiBer (eds.):UsingCorporatoExplore
LinguisticVariation.2002.xii,275pp.8 stenströM, Anna-Brita, Gisle Andersen and ingrid kristine hAsUnd:TrendsinTeenageTalk.
Corpuscompilation,analysisandfindings.2002.xii,229pp.7 AltenBerG, Bengt and sylviane GrAnGer (eds.):LexisinContrast.Corpus-basedapproaches.2002.
x,339pp.6 toGnini-Bonelli, elena:CorpusLinguisticsatWork.2001.xii,224pp.5 GhAdessy, Mohsen, Alex henry and robert l. roseBerry (eds.):SmallCorpusStudiesandELT.
Theoryandpractice.2001.xxiv,420pp.4 hUnston, susan and Gill FrAnCis:PatternGrammar.Acorpus-drivenapproachtothelexical
grammarofEnglish.2000.xiv,288pp.3 Botley, simon philip and tony Mcenery (eds.):Corpus-basedandComputationalApproachesto
DiscourseAnaphora.2000.vi,258pp.2 pArtinGton, Alan:PatternsandMeanings.UsingcorporaforEnglishlanguageresearchandteaching.
1998.x,158pp.1 peArson, Jennifer:TermsinContext.1998.xii,246pp.