Racial Inequities in Canadian Academia: The Case for Examining Within Discipline Variation
Bibliographic record
Abstract
Racialized faculty often earn less than their white counterparts due to a lifetime of structural, social, and institutional inequities presenting barriers to success within a given university. This research aims to explore the mechanisms and processes that help to explain the expected residual differences in income for faculty based on race, gender, and discipline. Focusing on one Southwestern Ontario university, I control for several factors such as faculty members' salaries over 5 years, year of graduation, type of degree, rank, type of employment, number of job changes, appointment, gender, visible minority status, and citations. Publicly available data was compiled from the 2016-2020 Ontario public sector salary disclosure list, faculty websites, CVs, Google Scholar profiles, and LinkedIn. Findings showed variation in income among disciplines by race, with racialized faculty earning more in the sciences compared to the arts. Addressing this gap in the literature will have future implications for research, EDI initiatives, and policy intervention. By using a different methodology than previous research, this study can help address and mitigate some of the limitations found with using self-reported and survey data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 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".