The status of women cognitive scientists in Canada: Insights from publicly available NSERC funding data.
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 life span (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) versus 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. (PsycINFO Database Record
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.030 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".