Gender as a Variable to StudyAcademic Writing

Guillaume Cabanacguillaume.cabanac@univ-tlse3.fr

April 27, 2017

http://bit.ly/smsCabanac2017

Topic 2 : Indexing and Information Retrieval

IRIS Team: Information Retrieval & Information Synthesis

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1. Prelude on Text and Data Mining

2. Gender as a Variable

3. Solo/Multi-authorships as a Variable

4. Conclusion

Gender as a Variableto Study Academic Writing

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Questioning scientific texts1 — Prelude on Text and Data Mining

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Questioning scientific texts

Academic Search

Information Retrieval

Exploratory Data Analysis

Scientometrics

1 — Prelude on Text and Data Mining

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1 — Prelude on Text and Data Mining

What’s in my Toolbox

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MertonPrice

Tukey

Hartley Hubert Milard

1 — Prelude on Text and Data Mining

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In Search of “Opportunity Structures”

Research Question:- Compelling- Original / Unexpected- Important (impact)

Literature Review:- Interdisciplinary- Wide timespan

Data and Methods:- Original data, preferably open- Mixed methods: Quantitative + qualitative

1 — Prelude on Text and Data Mining

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CollaborationScientometrics 2015a

Work-life BalanceJASIST 2013a

Peer ChoiceBJET 2017

Partnerships j-indexScientometrics 2013

Downright FuriesLearned Publishing 2017

Order EffectsJASIST 2013b

RecommendationsTopic + Social

Scientometrics 2011

1 — Prelude on Text and Data Mining

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Eponym ExtractionScientometrics 2014b

Tables/Figures: Solo vs Multiauthor JASIST 2014

Tables/Figures: ♀ vs♂Scientometrics 2014a

Landscape of a ResearchField via Gatekeepers

JASIST 2012

Unconventional Academic WritingFestschrift J. Hartley 2015

A Lifetime of WritingScientometrics 2015b

Festschrift A. Schubert 2016

1 — Prelude on Text and Data Mining

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1. Prelude on Text and Data Mining

2. Gender as a Variable

3. Solo/Multi-authorships as a Variable

4. Conclusion

Gender as a Variableto Study Academic Writing

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Academic Writing – Gender Study

Theory in Psychology (1960’s) Men are more spatially and mathematically oriented than women

Women are more verbally oriented than men

2 — Gender as a Variable

Hartley, J. & Cabanac, G. (2014). Do men and women differ in their use of tables and graphs in academic publications?Scientometrics, 98, 2, 1161-1172.

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Data Scrapping and Crunching

2 — Gender as a Variable

8,336 journals3,576 single-authored

papers in 2011

1,682 papers with1+ figure(s)

♀ or♂ ?

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Academic Writing – Gender Study

Our findings on 1,403 single-authored articles in science Men use 26% more figures than Women (p < 0.001)

Men use 11% more tables than Women (p = 0.102)

… but is the difference practical?

2 — Gender as a Variable

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1. Prelude on Text and Data Mining

2. Gender as a Variable

3. Solo/Multi-authorships as a Variable

4. Conclusion

Gender as a Variableto Study Academic Writing

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Academic Writing – Study of Collaborations

The “Friday” Hypothesis When writing in groups, it is harder to agree on text than on figures/graphs

More Figures and Graphs in multi-author papers?

3 — Solo/Multi-authorships as a Variable

Cabanac, G., Hubert, G., & Hartley, J. (2014). Solo versus collaborative writing: Discrepancies in the use of tables and graphin academic articles. Journal of the American Society for Information Science and Technology, 65, 4, 812–820.

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Academic Writing – Study of Collaborations

More tables in multi-author vs. single-author papers

3 — Solo/Multi-authorships as a Variable

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Academic Writing – Study of Collaborations

More figures in multi-author vs. single-author papers

3 — Solo/Multi-authorships as a Variable

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1. Prelude on Text and Data Mining

2. Gender as a Variable

3. Solo/Multi-authorship as a Variable

4. Conclusion

Gender as a Variableto Study Academic Writing

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Take-home Message

4 — Conclusion

We studied the frequency of tabs/figs in papers

Men use more of these (a fraction) compared to Women

But the difference is much higher between Solo- and Multi-authorships

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Text and Data Mining: My views/wishes/hopes

What: TDM and Scientometrics

Evaluation side

Analytic side

Why: Shed some (more) light on

... The social worlds in Science

... Knowledge accumulation

... Knowledge flows

How : a range of opportunities

... Open datasets (i4oc.org, scienceopen.com...)

... Interdisciplinary research

... Varied and complementary methods

4 — Conclusion

Recommended Bedside Reading ;-)

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20142011 2014

4 — Conclusion

Merci

http://www.irit.fr/~Guillaume.Cabanac

@gcabanac