The status of women cognitive scientists in Canada: Insights from publicly available NSERC funding data
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
A crucial question within science and academia, and cognitive science specifically, is whether there is gender disparity in opportunity and advancement over the professional lifespan (e.g., Ceci, Ginther, Kahn, & Williams, 2014; Geraci, Balsis, & Busch, 2015; Valian, 1998). To investigate this question, we analyzed gender distributions in publicly available federal funding data from the Natural Sciences and Engineering Research Council (NSERC) of Canada that are specific to cognitive psychology and cognitive neuroscience. There were three key results. First, the proportion of women cognitive scientists progressively diminished at each career stage, particularly at the transition between graduate and postdoctoral studies. Second, female principal investigators (PI) received smaller average Discovery Grant amounts, and were less likely to receive Discovery Accelerator Supplements as a proportion of all Discovery Grants funded. Finally, at the PI level, gender differences were relatively smaller for institution-initiated grants (i.e., Canada Research Chairs) vs. investigator-initiated grants (i.e., Discovery Grants). It is our hope that presentation of such data, in concert with other recent reports for our field (e.g., Klatzky, Holt, & Behrmann, 2015; Peelle, 2016; Vaid & Geraci, 2016), continues to raise awareness that gender parity issues remain a concern that deserves ongoing attention within the field of cognitive science in Canada.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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