Intersectional Study of the Gender Gap in STEM through the Identification of Missing Datasets about Women: A Multisided Problem
Source abstract
Abstract
This paper discusses the problem of missing datasets for analysing and exhibiting the role of women in STEM with a particular focus on computer science (CS), artificial intelligence (AI) and data science (DS). It discusses the problem in a concrete case of a global south country (i.e., Mexico). Our study aims to point out missing datasets to identify invisible information regarding women and the implications when studying the gender gap in different STEM disciplines. Missing datasets about women in STEM show that the first step to understanding gender imbalance in STEM is building women’s history by “completing” existing datasets.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Career Development and Diversity
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
CNRS, University Lyon, INSA Lyon, UCBL, Ecole Centrale Lyon, University Lyon 2, LIRIS, UMR5205, 69622 Villeurbanne, France
Funding
CNRS, France
References (53)
Show source-provided references
1. Hill, C., Corbett, C., and St Rose, A. (2010). Why So Few? Women in Science, Technology, Engineering, and Mathematics, American Association of University Women.
2. Keller, E.F., and Scharff-Goldhaber, G. (1987). Reflections on Gender and Science, American Association of Physics Teachers.10.1119/1.15186 ↗
3. Lehming, R.F., Alt, M.N., Chen, X., Hall, L., Burton, L., Burrelli, J.S., Kannankutty, N., Proudfoot, S., Regets, M.C., and Boroush, M. (2010). Science and Engineering Indicators 2010, NSB 10-01.
4. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor, St. Martin’s Press.
5. Doing gender in engineering workplace cultures. II. Gender in/authenticity and the in/visibility paradox10.1080/19378620903225059 ↗
6. Young, E., Wajcman, J., and Sprejer, L. (2021). Where Are the Women? Mapping the Gender Job Gap in AI, The Alan Turing Institute. Policy Briefing: Full Report.
7. Berman, F.D., and Bourne, P.E. (2015). Let’s make gender diversity in data science a priority right from the start. PLoS Biol., 13.10.1371/journal.pbio.1002206 ↗
8. Gender-fluid geek girls: Negotiating inequality regimes in the tech industry10.1177/0891243216680590 ↗
9. Margolis, J., and Fisher, A. (2002). Unlocking the Clubhouse: Women in Computing, MIT Press.
10. Women in science in Germany10.1126/science.286.5447.2081 ↗
11. Women in science in Germany10.1017/S0269889702000649 ↗
12. Mello, P.A. (2022). Qualitative Comparative Analysis: An Introduction to Research Design and Application, Georgetown University Press.
13. The gender gap in STEM fields: The impact of the gender stereotype of math and science on secondary students’ career aspirations10.3389/feduc.2019.00060 ↗
14. Fayyad, U., and Hamutcu, H. (2022, June 01). Toward Foundations for Data Science and Analytics: A Knowledge Framework for Professional Standards. Available online: https://hdsr.mitpress.mit.edu/pub/6wx0qmkl/release/4?readingCollection=70ac5c46.
15. Female performance and participation in computer science: A national picture
16. STEM pathways: Do men and women differ in why they enter and exit?10.1177/2332858417727276 ↗
17. D’ignazio, C., and Klein, L.F. (2020). Data Feminism, MIT Press.10.7551/mitpress/11805.001.0001 ↗
18. Hearn, J., and Louvrier, J. (2015). Theories of difference, diversity, and intersectionality. The Oxford Handbook of Diversity in Organizations, Oxford University Press.10.1093/oxfordhb/9780199679805.013.28 ↗
19. Race after technology: Abolitionist tools for the new jim code
20. Does diversity pay? Race, gender, and the business case for diversity10.1177/000312240907400203 ↗
21. It’s all in the family: Intersections of gender, race, and nation10.1111/j.1527-2001.1998.tb01370.x ↗
22. Gender discrimination at work: Connecting gender stereotypes, institutional policies, and gender composition of workplace10.1177/0891243211424741 ↗
23. Wajcman, J., Young, E., and Fitzmaurice, A. (2020). The Digital Revolution: Implications for Gender Equality and Women’s Rights 25 Years after Beijing, United Nations.
24. Myers West, S. (2020). Discriminating Systems: Gender, Race and Power in Artificial Intelligence, Georgia Institute of Technology.
25. Freire, A., Porcaro, L., and Gómez, E. (2021, January 21–31). Measuring diversity of artificial intelligence conferences. Proceedings of the Artificial Intelligence Diversity, Belonging, Equity, and Inclusion, Online.
26. Abbate, J. (2012). Recoding Gender: Women’s Changing Participation in Computing, MIT Press.10.7551/mitpress/9014.001.0001 ↗
27. Escalator or step stool? Gendered labor and token processes in tech work10.1177/0891243219835737 ↗
28. UNESCO (2020). Artificial Intelligence and Gender Equality: Key Findings of UNESCO’S Global Dialogue, UNESCO.
29. Career paths in engineering firms: Gendered patterns and implications10.1177/1069072716679987 ↗
30. Ensmenger, N.L. (2012). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise, MIT Press.
31. Foulds, J.R., Islam, R., Keya, K.N., and Pan, S. (2020, January 20–24). An intersectional definition of fairness. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.10.1109/ICDE48307.2020.00203 ↗
32. Simonite, T. (2022, June 01). AI Is the Future—But Where Are the Women. Available online: https://www.wired.com/story/artificial-intelligence-researchers-gender-imbalance.
33. Why diversity programs fail
34. Buolamwini, J., and Gebru, T. (2018, January 23–24). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability and Transparency, New York, NY, USA.
35. Wajcman, J. (1991). Feminism Confronts Technology, Penn State Press.
36. Feminist theories of technology10.1093/cje/ben057 ↗
37. Hidden Figures: The American Dream and the Untold Story of the Black Women Mathematicians Who Helped Win the Space Race by Margot Lee Shetterly10.1353/ahc.2017.0026 ↗
38. The Glass Universe: How the Ladies of the Harvard Observatory Took the Measure of the Stars
39. Rise of the Rocket Girls: The Women Who Propelled Us, from Missiles to the Moon to Mars10.1038/532034a ↗
40. When computers were women10.1353/tech.1999.0128 ↗
41. Coello, C.A.C. (2003). Breve Historia de la Computación y Sus Pioneros, Fondo de Cultura Económica.
42. Copeland, B.J. (2022, June 01). The Modern History of Computing. Available online: https://plato.stanford.edu/entries/computing-history/.
43. Women in computing around the world10.1145/543812.543839 ↗
44. Vela, B., Cavero, J.M., Vargas-Solar, G., Espinosa-Oviedo, J.A., and Cáceres, P. (2021). A Geo-Gender Study of Indexed Computer Science Research Publications. arXiv.
45. Valian, V. (1999). Why So Slow? The Advancement of Women, MIT Press.10.7551/mitpress/7235.001.0001 ↗
46. The Athena factor: Reversing the brain drain in science, engineering, and technology
47. Women in Engineering: A review of the 2005 literature
48. Interactive, H. Attitudes and Experiences of Engineering Alumni, Prepared for the Society of Women Engineers, unpublished.
49. Penalties for success: Reactions to women who succeed at male gender-typed tasks10.1037/0021-9010.89.3.416 ↗
50. Why are women penalized for success at male tasks? The implied communality deficit10.1037/0021-9010.92.1.81 ↗
51. McClure, C.R. (2007). A Test of Leadership: Charting the Future of US Higher Education.
52. Committee on Prospering in the Global Economy of the 21st Century (2007). Rising above the Gathering Storm: Energizing and Employing America for a Brighter Economic Future, National Academies Press.
53. Ashby, C. (2006). Science, technology, engineering, and mathematics: Trends and the role of federal programs. Testimony before the Committee on Education and the Workforce, House of Representatives, National Center on Educational Outcomes (NCEO).