Closing the publishing gender gap in economics and political science: Does a critical mass matter?
Bibliographic record
Abstract
Using novel data on publications and citations by researchers in economics and political science in top 50 universities globally, we analyze the relationship between female representation and the gender gap in research output and impact. Using the concepts of substantive representation and critical mass, we expect that the average female researcher in departments with more women publishes more and receives more citations than the average female researcher in universities with a lower share of female faculty. Comparing women's publication and citation performance for the top 50 universities across the globe relative to men's, we find support for these expectations. We find that female researchers' performance matches that of their male counterparts in balanced departments, which are more common in political science. In contrast, the link between female performance and representation is weaker in departments with little gender balance, which are more typical in economics. These findings highlight the importance of reaching a critical mass of female representation to close gender gaps in research output and impact.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".