Conférence au workshop “Women and men in science: Do we need gender metrics?” du 27 avril 2017 à l'Université Toulouse 2 - Jean Jaurès https://www.irit.fr/~Guillaume.Cabanac/docs/workshopGenderScienceLabexSMS2017.pdf

1. Gender as a Variable to Study
Academic Writing
Guillaume Cabanac
guillaume.cabanac@univ-tlse3.fr
April 27, 2017
http://bit.ly/smsCabanac2017
 Topic 2 : Indexing and Information Retrieval
IRIS Team: Information Retrieval & Information Synthesis

2. 2
1. Prelude on Text and Data Mining
2. Gender as a Variable
3. Solo/Multi-authorships as a Variable
4. Conclusion
Gender as a Variable
to Study Academic Writing

3. 3
Questioning scientific texts
1 — Prelude on Text and Data Mining

4. 4
Questioning scientific texts
Academic Search
Information Retrieval
Exploratory Data Analysis
Scientometrics
1 — Prelude on Text and Data Mining

5. 5
1 — Prelude on Text and Data Mining

6. What’s in my Toolbox
6
Merton
Price
Tukey
Hartley Hubert Milard
1 — Prelude on Text and Data Mining

7. 7
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

8. 8
Collaboration
Scientometrics 2015a
Work-life Balance
JASIST 2013a
Peer Choice
BJET 2017
Partnerships j-index
Scientometrics 2013
Downright Furies
Learned Publishing 2017
Order Effects
JASIST 2013b
Recommendations
Topic + Social
Scientometrics 2011
1 — Prelude on Text and Data Mining

9. 9
Eponym Extraction
Scientometrics 2014b
Tables/Figures: Solo vs Multiauthor
JASIST 2014
Tables/Figures: ♀ vs ♂
Scientometrics 2014a
Landscape of a Research
Field via Gatekeepers
JASIST 2012
Unconventional Academic Writing
Festschrift J. Hartley 2015
A Lifetime of Writing
Scientometrics 2015b
Festschrift A. Schubert 2016
1 — Prelude on Text and Data Mining

10. 10
1. Prelude on Text and Data Mining
2. Gender as a Variable
3. Solo/Multi-authorships as a Variable
4. Conclusion
Gender as a Variable
to Study Academic Writing

11. 11
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.
http://www.kaheel7.com/eng/images/stories/2(5).jpg

12. 12
Data Scrapping and Crunching
2 — Gender as a Variable
8,336 journals
3,576 single-authored
papers in 2011
1,682 papers with
1+ figure(s)
♀ or ♂ ?

13. 13
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

14. 14
1. Prelude on Text and Data Mining
2. Gender as a Variable
3. Solo/Multi-authorships as a Variable
4. Conclusion
Gender as a Variable
to Study Academic Writing

15. 15
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 graph
in academic articles. Journal of the American Society for Information Science and Technology, 65, 4, 812–820.

16. 16
Academic Writing – Study of Collaborations
 More tables in multi-author vs. single-author papers
3 — Solo/Multi-authorships as a Variable

17. 17
Academic Writing – Study of Collaborations
 More figures in multi-author vs. single-author papers
3 — Solo/Multi-authorships as a Variable

18. 18
1. Prelude on Text and Data Mining
2. Gender as a Variable
3. Solo/Multi-authorship as a Variable
4. Conclusion
Gender as a Variable
to Study Academic Writing

19. 19
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

20. 20
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

21. Recommended Bedside Reading ;-)
21
20142011 2014
4 — Conclusion

22. Merci
http://www.irit.fr/~Guillaume.Cabanac
@gcabanac