@inproceedings{chen-2026-gender,
title = "Gender-Competence Associations in Computer Science Discourse: A Longitudinal Analysis",
author = "Chen, Lauren",
editor = "Fale{\'n}ska, Agnieszka and
Basta, Christine and
Costa-juss{\`a}, Marta and
Sta{\'n}czak, Karolina and
Nozza, Debora and
Attanasio, Giuseppe and
Neplenbroek, Vera and
Hardmeier, Christian",
booktitle = "Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing ({G}e{BNLP})",
month = nov,
year = "2026",
address = "Hengqin, China (remote)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.gebnlp-1.1/",
pages = "1--6",
ISBN = "979-8-89176-447-7",
abstract = "Women remain underrepresented in computer science, yet little is known about how gendered associations within the field have changed over time. This study examines longitudinal patterns in gender{--}competence associations across academic and practitioner discourse from 2010{--}2025 using 210 Word2Vec models trained on 9.6 billion tokens from arXiv computer science papers as well as Stack Overflow posts and comments. Female-competence associations were measured using relative norm distance (RND) between female, male, and competence wordsets. In academic discourse, relative female-competence associations decreased significantly over time ($\tau= -0.717, p < 0.001$), reflecting a shift towards stronger relative-male competence association. In contrast, practitioner discourse showed no significant longitudinal trend ($\tau = -0.250, p = 0.192$). Academic gender-competence trends were significantly associated with women{'}s representation in the U.S. computer science workforce whereas practitioner trends were not. These findings demonstrate that gendered linguistic associations have not evolved uniformly across computer science and highlight the importance of discourse context when evaluating longitudinal changes in gendered language."
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<abstract>Women remain underrepresented in computer science, yet little is known about how gendered associations within the field have changed over time. This study examines longitudinal patterns in gender–competence associations across academic and practitioner discourse from 2010–2025 using 210 Word2Vec models trained on 9.6 billion tokens from arXiv computer science papers as well as Stack Overflow posts and comments. Female-competence associations were measured using relative norm distance (RND) between female, male, and competence wordsets. In academic discourse, relative female-competence associations decreased significantly over time (τ= -0.717, p < 0.001), reflecting a shift towards stronger relative-male competence association. In contrast, practitioner discourse showed no significant longitudinal trend (τ = -0.250, p = 0.192). Academic gender-competence trends were significantly associated with women’s representation in the U.S. computer science workforce whereas practitioner trends were not. These findings demonstrate that gendered linguistic associations have not evolved uniformly across computer science and highlight the importance of discourse context when evaluating longitudinal changes in gendered language.</abstract>
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%0 Conference Proceedings
%T Gender-Competence Associations in Computer Science Discourse: A Longitudinal Analysis
%A Chen, Lauren
%Y Faleńska, Agnieszka
%Y Basta, Christine
%Y Costa-jussà, Marta
%Y Stańczak, Karolina
%Y Nozza, Debora
%Y Attanasio, Giuseppe
%Y Neplenbroek, Vera
%Y Hardmeier, Christian
%S Proceedings of the 7th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China (remote)
%@ 979-8-89176-447-7
%F chen-2026-gender
%X Women remain underrepresented in computer science, yet little is known about how gendered associations within the field have changed over time. This study examines longitudinal patterns in gender–competence associations across academic and practitioner discourse from 2010–2025 using 210 Word2Vec models trained on 9.6 billion tokens from arXiv computer science papers as well as Stack Overflow posts and comments. Female-competence associations were measured using relative norm distance (RND) between female, male, and competence wordsets. In academic discourse, relative female-competence associations decreased significantly over time (τ= -0.717, p < 0.001), reflecting a shift towards stronger relative-male competence association. In contrast, practitioner discourse showed no significant longitudinal trend (τ = -0.250, p = 0.192). Academic gender-competence trends were significantly associated with women’s representation in the U.S. computer science workforce whereas practitioner trends were not. These findings demonstrate that gendered linguistic associations have not evolved uniformly across computer science and highlight the importance of discourse context when evaluating longitudinal changes in gendered language.
%U https://aclanthology.org/2026.gebnlp-1.1/
%P 1-6
Markdown (Informal)
[Gender-Competence Associations in Computer Science Discourse: A Longitudinal Analysis](https://aclanthology.org/2026.gebnlp-1.1/) (Chen, GeBNLP 2026)
ACL