Gender and language use in scientific grant writing
Bibliographic record
Abstract
Women in science, technology, engineering, and math are not equally represented across tenure-track career stages, and this extends to grant funding, where women applicants often have lower success rates compared with men. While gender bias in reviewers has been documented, it is currently unknown whether written language in grant applications varies predictably with gender to elicit bias against women. Here we analyse the text of ∼2000 public research summaries from the 2016 Natural Sciences and Engineering Research Council (NSERC) individual Discovery Grant (DG) program. We explore the relationship between language variables, inferred gender and career stage, and funding levels. We also analyse aggregated data from the 2012–2018 NSERC DG competitions to determine whether gender impacted the probability of receiving a grant for early-career researchers. We document a marginally significant gender difference in funding levels for successful grants, with women receiving $1756 less than men, and a large and significant difference in rejection rates among early-career applicants (women: 40.4% rejection; men: 33.0% rejection rate). Language variables had little ability to predict gender or funding level using predictive modelling. Our results indicate that NSERC funding levels and success rates differ between men and women, but we find no evidence that gendered language use affected funding outcomes.
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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.070 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".