Disparities in Tenure and Promotion Outcomes Among Racialized and Female Faculty in Canadian Universities
Bibliographic record
Abstract
Universities are often seen as inclusive and liberal spaces where equity and social justice prevail. Despite this ideal image, racial and gender disparities continue to persist and have been documented. Racialized faculty are less likely to be university professors (Ramos, 2012) and have lower earnings (Li, 2012). Similarly, female professors are less likely to be promoted (Nakhaie, 2007; Stewart, Ornstein & Drakich, 2009) and experience significant wage gaps compared to their male colleagues (Momani, Dreher & Williams, 2019). Drawing on original survey data from the University, Tenure, Promotion and Hiring (UTPH) survey, this dissertation examines inequities in promotion for racialized and female Canadian faculty at different stages of their career (e.g., tenure, promotion to associate professor, promotion to full professor). It also looks at commonly-cited explanations such as human capital theory, cultural or identity taxation, and glass ceiling theory to see if they can be used to adequately explain the disparities in promotion that exist. Finally, this dissertation examines perceptions of the factors that influence tenure, promotion, and hiring to examine whether racialized faculty see the academy differently from their non-racialized counterparts. When examining the chapters in this dissertation collectively, it is clear that there are systemic inequalities that exist within universities that affect the career trajectories of racialized and female faculty in Canada. This dissertation concludes with a critical examination of various institutional responses in recent times and future directions for research.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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 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".