Explaining Gender Gap Variation in Political Science Knowledge Production
Bibliographic record
Abstract
ABSTRACT When we open a random political science journal, we have a roughly two-to-one chance that the article is written by a man. Beyond this general finding, we know little about the gender gaps within political science knowledge production: Are women more represented in lower- or higher-ranked journals? Do they publish more single-authored or multiauthored papers? Do they publish more content in some fields than in others? This article answers these questions by analyzing an original dataset based on the International Political Science Abstracts (a peer-reviewed academic journal) from 2022 consisting of more than 7,000 articles and more than 13,000 authors in political science from around the world. We find no difference in the percentage of female authors between higher- and lower-ranked journals. We find a slightly higher propensity among women to publish in teams. Regarding subfields of study, women are particularly underrepresented in political theory, in which they publish only 21.6% of all published articles—which is an approximate 12-percentage-point deviation from the overall average.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".