Bibliographic record
Abstract
Increasing the number of female evaluators could help female candidates if evaluators prefer candidates of their own gender. I study whether there is any evidence of such preferences with a unique data set containing 10,500 scores given by 105 evaluators to 3,500 students in the humanities and social sciences who applied for a doctoral scholarship. On average, I find very weak evidence of same-gender preferences for male evaluators ( p = 0.133). To better understand this effect, I also study same-gender preferences across the distribution of candidates, in subcommittees with different gender composition, and for evaluators from different disciplines. I show that male evaluators give higher scores to strong male candidates relative to those given by female evaluators. At the same time, male evaluators give higher scores to male candidates than do female evaluators when there is only one male evaluator in the subcommittee. The representation of men in a discipline does not seem to affect the scores given by evaluators. Overall, there is no clear evidence that replacing a male evaluator with a female one would help female candidates.
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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".