Out of sight, out of mind: underrepresentation of racialized faculty in Canadian psychology
Bibliographic record
Abstract
Psychologists of colour (herein referred to as BIPOC—Black, Indigenous, and People of Colour) contribute to diverse perspectives and also conduct critical research that addresses the significant disparities and challenges faced by communities of colour in accessing mental healthcare services. There has been some concern that BIPOC psychologists are underrepresented in academia, but this issue has yet to be evaluated in a Canadian context due to a lack of available data. This study examined the racial demographics of psychology faculty across 23 major universities in Ontario, Canada (n = 1421), the province with the largest number of universities. White psychologists are overwhelmingly overrepresented compared to BIPOC psychologists, reflecting significant underrepresentation relative to the province’s population. White faculty predominantly hold secure academic positions (tenured, tenure track) while BIPOC faculty are concentrated in precarious roles (adjunct, sessional, lecturer). Professors of East Asian heritage constituted the largest group among BIPOC faculty. Additionally, BIPOC psychologists are underrepresented across all professional subspecialties. Systemic racism, historical biases, and exclusionary practices were identified as major barriers. Our findings call for urgent reforms in university hiring practices and psychology training programmes to reflect the diversity of the population they serve and to dismantle systemic barriers that perpetuate racial inequalities in academia.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".