Gender gaps in research productivity and recognition among elite scientists in the U.S., Canada, and South Africa
Bibliographic record
Abstract
This study builds upon the literature documenting gender disparities in science by investigating research productivity and recognition among elite scientists in three countries. This analysis departs from both the general comparison of researchers across organizational settings and academic appointments on one hand, and the definition of "elite" by the research outcome variables on the other, which are common in previous studies. Instead, this paper's approach considers the stratification of scientific careers by carefully constructing matched samples of men and women holding research chairs in Canada, the United States and South Africa, along with a control group of departmental peers. The analysis is based on a unique, hand-curated dataset including 943 researchers, which allows for a systematic comparison of successful scientists vetted through similar selection mechanisms. Our results show that even among elite scientists a pattern of stratified productivity and recognition by gender remains, with more prominent gaps in recognition. Our results point to the need for gender equity initiatives in science policy to critically examine assessment criteria and evaluation mechanisms to emphasize multiple expressions of research excellence.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".