Women in the Shadow of Big Men: The Case of Canada Excellence Research Chairs
Bibliographic record
Abstract
Canada Excellence Research Chairs program—an award worth up to ten million dollars over seven years to attract and support world-renowned researchers and their teams to establish research programs in Government of Canada’s science and technology priority areas at Canadian universities—has been one of the most controversial governmental funding allocations in Canada. One of the main criticisms to this program is the absence of clear selection and recruitment criteria, including promulgation of standards for inclusion and diversity, which have resulted in lack of representation of women among Chairs. The main purpose of this study is to shed light on gender differences in scientific production and impact of publications induced by Canada Excellence Research Chairs program and to examine co-authorship collaboration patterns that are formed as a result of introduction of this program. Findings reveal that when Chairs are listed as main investigators of the scientific work (either last or corresponding authors), female-led papers receive higher rate of citations and are published in journals with higher impact. Although citation impact of papers that include collaborations with women are the highest, more than 78% of researchers of each gender repeat their collaborations, with their male peers on authoring more than one papers. Last but not least, this study concludes that collaborations with women are fragile and are dependent on the presence of central male researchers. Therefore, contributions of women to high impact research is effective as long as they are under the shadow of more central, influential and popular men.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.045 | 0.018 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".