Female grant applicants are equally successful when peer reviewers assess the science, but not when they assess the scientist
Bibliographic record
Abstract
ABSTRACT Background Previous research shows that men often receive more research funding than women, but does not provide empirical evidence as to why this occurs. In 2014, the Canadian Institutes of Health Research (CIHR) created a natural experiment by dividing all investigator-initiated funding into two new grant programs: one with and one without an explicit review focus on the caliber of the principal investigator. Methods We analyzed application success among 23,918 grant applications from 7,093 unique principal investigators in a 5-year natural experiment across all investigator-initiated CIHR grant programs in 2011-2016. We used Generalized Estimating Equations to account for multiple applications by the same applicant and an interaction term between each principal investigator’s self-reported sex and grant programs to compare success rates between male and female applicants under different review criteria. Results The overall grant success rate across all competitions was 15.8%. After adjusting for age and research domain, the predicted probability of funding success in traditional programs was 0.9 percentage points higher for male than for female principal investigators (OR 0.934, 95% CI 0.854-1.022). In the new program focused on the proposed science, the gap was 0.9 percentage points in favour of male principal investigators (OR 0.998, 95% CI 0.794-1.229). In the new program with an explicit review focus on the caliber of the principal investigator, the gap was 4.0 percentage points in favour of male principal investigators (OR 0.705, 95% CI 0.519- 0.960). Interpretation This study suggests gender gaps in grant funding are attributable to less favourable assessments of women as principal investigators, not differences in assessments of the quality of science led by women. We propose ways for funders to avoid allowing gender bias to influence research funding. Funding This study was unfunded.
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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 | 0.072 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".