Does double-blind peer review effectively correct for gender disparities in research funding?
Bibliographic record
Abstract
New mechanisms for mitigating biases in grant peer review have been proposed. One such mechanism is blinding reviewers to the identity of grant applicants, but evidence of its effectiveness remains scarce. We leverage detailed information on more than 2,000 applications to the Villum Foundation’s “Villum Experiment”, a double-blinded grant scheme, to evaluate how blinding can reduce gender bias in research funding. We show that small gender differences can persist despite an effective double-blinded evaluation of applications. These differences are likely caused by differences in gender compositions across disciplines, and a strong underrepresentation of highly experienced women among the applicants and in the population in general. Our analysis highlights how policies aimed at leveling demographic disparities in research funding rates may eliminate direct bias but fall short of confronting broader structural inequalities.
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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 | 0.597 | 0.803 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".