The Gender Gap in Wikipedia Talk Pages
Bibliographic record
Abstract
Wikipedia is an important source of information in today's world. Yet, the lack of gender diversity in its community has been shown to affect the topics covered. Each Wikipedia article has a talk page that volunteer editors use to discuss proposed changes. Research on the gender bias has focused on article contribution and topic coverage, but not talk page activity. It has been suggested that the conflicts that take place in talk pages are especially intimidating for women, but this assertion has not been quantified yet. To fill this gap, we collected a dataset of all comments on Wikipedia talk pages, enriching it with gender information available from users who have chosen to disclose their gender on their user profiles or settings. Among the users active in talk pages, 49,387 indicated that they are male while only 5,996 indicated that they are female. The comments of these users make up for 4 million comments, approximately one quarter of all comments on Wikipedia. In addition, we observed that female participation varies by topic, reflecting traditional gender stereotypes: compared to Science, Technology, Engineering and Mathematics (STEM) topics, women were more active in categories such as Gender studies or Feminism. Results also indicate that a post on a talk page is 2.4\% less likely to be replied to if the author is female. Likewise, reply probability varies from topic to topic. These results provide quantitative support for a gender bias in Wikipedia talk pages, and serve as a basis for discussing why overall female participation is low.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".