Gendered Citation Patterns across Political Science and Social Science Methodology Fields
Bibliographic record
Abstract
Accumulated evidence identifies discernible gender gaps across many dimensions of professional academic careers including salaries, publication rates, journal placement, career progress, and academic service. Recent work in political science also reveals gender gaps in citations, with articles written by men citing work by other male scholars more often than work by female scholars. This study estimates the gender gap in citations across political science subfields and across methodological subfields within political science, sociology, and economics. The research design captures variance across research areas in terms of the underlying distribution of female scholars. We expect that subfields within political science and social science disciplines with more women will have smaller gender citation gaps, a reduction of the “Matthew effect” where men’s research is viewed as the most central and important in a field. However, gender citation gaps may persist if a “Matilda effect” occurs whereby women’s research is viewed as less important or their ideas are attributed to male scholars, even as a field becomes more diverse. Analysing all articles published from 2007–2016 in several journals, we find that female scholars are significantly more likely than mixed gender or male author teams to cite research by their female peers, but that these citation rates vary depending on the overall distribution of women in their field. More gender diverse subfields and disciplines produce smaller gender citation gaps, consistent with a reduction in the “Matthew effect”. However, we also observe undercitation of work by women, even in journals that publish mostly female authors. While improvements in gender diversity in academia increase the visibility and impact of scholarly work by women, implicit biases in citation practices in the social sciences persist.
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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 | 0.011 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".