The status of women cognitive scientists in Canada 6 years later: Insights from publicly available Natural Sciences and Engineering Research Council of Canada (NSERC) funding data.
Bibliographic record
Abstract
This article examines gender disparities in the allocation of Natural Sciences and Engineering Research Council of Canada (NSERC) funding to cognitive scientists. It expands on previous work by Titone et al. (2018) with updated data from 2016 to 2023. By analyzing publicly available funding data, we assessed trends in NSERC awards distribution across different career stages. Our analyses revealed that while women receive more student research awards at the undergraduate and graduate levels, significant gender disparities persist at senior levels, where men continue to receive more Discovery Grants and higher funding amounts. With respect to changes in recent years, the postdoctoral fellowship awards showed increased gender parity. As well, early career researchers have seen a shift towards gender parity, indicating some success in the efforts to support up-and-coming researchers. Thus, while progress has been made, further actions are necessary to bridge gender gaps in research funding and to support the long-term career development of women cognitive scientists. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.018 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".