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Record W4411380535 · doi:10.20935/mhealthwellb7762

Out of sight, out of mind: underrepresentation of racialized faculty in Canadian psychology

2025· article· en· W4411380535 on OpenAlexaffabout
Sonya C. Faber, Dana Strauss, Monnica T. Williams

Bibliographic record

VenueAcademia Mental Health and Well-Being · 2025
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSightPsychologySociologyPhysicsAstronomy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.452
Teacher spread0.411 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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