Gender disparity in funding rates in double-blind grant peer review: The case of the Villum Experiment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.217 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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 it