The Gender Publication Gap Revisited: Evidence from the <i>International Political Science Review</i>
Bibliographic record
Abstract
ABSTRACT Since the 1990s, there has been consensus in the literature of a submission and publication gap that favors men. Important research in the intervening years has explored the many reasons for this output gap: imbalanced administrative workloads; bias in top journals against female-dominated subfields and methodological approaches; and lower confidence levels among women, sometimes known as the “Matthew effect.” However, in the intervening period, there has been a notable emphasis on recruiting more women into academia, and the importance of publishing for career development has intensified. Journal case studies have highlighted a growth in output by women academics but show that men are still overrepresented. Using a case study of the International Political Science Review (IPSR), we contribute to the emerging body of work that shows that the gender gap has diminished or even been eliminated. We present data on submissions and acceptances by gender, and we base our comparisons in the gender balance of the departments of submitting authors. The results are clear, for IPSR, the gender gap has closed and women now publish on a par with their men colleagues in their department.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.231 | 0.583 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".