Gender disparity in funding rates in double-blind grant peer review: The case of the Villum Experiment
Bibliographic record
Abstract
Abstract The Villum Experiment (VEX) is one of the few funding schemes that employs a double-blind review process where applicants are blinded to reviewers, applications are highly standardized, reviewers do not deliberate, and funding is determined solely by ranked aggregated review scores. This unique controlled setting enables assumptions that direct reviewer gender bias is highly unlikely. Using a causal framework (DAG), we examine the extent to which gender disparities in funding may exist in such a setting. Our analyses of 2,041 applications from five funding rounds (2017–2021) reveal a small but consistent gender disparity in success rates, concentrated within the Life Science panel. As reviewer bias is unlikely in this setting, these disparities or structural inequalities are likely caused by differences in gender compositions across disciplines and the underrepresentation of highly experienced women among the applicants and in the population in general. Multilevel modeling with poststratification indicates that accounting for these structural factors removes the disparity in funding success rates. Our findings highlight that gender disparity in funding may remain without direct review bias. In this case, such remaining disparities are likely rooted in broader structural inequalities within academia and/or compositional effects.
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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 | 0.355 | 0.555 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".