Equity and Game-Theory Strategies to Promote Gender Diversity and Inclusion in an Academic Health Science Centre
Bibliographic record
Abstract
BACKGROUND: Achieving diversity, inclusion, and gender equity remains an elusive challenge for many institutions worldwide and is understudied in Canadian academic health science centres. METHODS: McMaster University's Department of Medicine undertook surveys and analyses to determine whether there was inequity in leadership positions and salaries, or unprofessional behaviour within the department. Measures of academic productivity in relation to gender for both educators and researchers were analyzed. The department began shifting policies to foster greater gender diversity and inclusion. A revision of the leadership selection process, incorporating tenets of equity and a new game theory-based strategy called Diversitive Agreement Versus Nash Equilibrium (DAvNE) was evaluated. RESULTS: The department's survey revealed underrepresentation of women and people of colour in leadership positions, with perceived barriers to their promotion. Both women and people of colour reported experiencing unprofessional behaviour directed toward them. A gender gap in base salary was observed, with female full professors being paid less. No difference in academic productivity was seen between male and female educators or researchers. The leadership competitions conducted under new selection processes emphasizing diversity resulted in 66% of participating women securing a leadership position, in comparison to 25% of participating men. People of colour made up 27% of members participating in these leadership competitions, but none was successful in obtaining a position. CONCLUSIONS: Diversity and inclusion disparities in the Department of Medicine at McMaster University indicate a need for further efforts and innovation to bring about greater gender and racial equity.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".