Racialized Faculty and Administrators in Ontario Colleges: A Case Study of Three Ontario Colleges
Bibliographic record
Abstract
Abstract Racialized Faculty and Administrators in Ontario Colleges:A Case Study of Three Ontario Colleges Nancy Harriette Simms Doctor of Education Department of Leadership, Higher and Adult Education Ontario Institute for Studies in Education University of Toronto 2024 There is an underrepresentation of racialized educators and administrators in Canada’s educational system. While this phenomenon has been researched at Canadian universities and elementary and secondary schools, there is a lacuna in the research at Canadian colleges. This dissertation addresses the gap by examining what contributes to the underrepresentation of racialized faculty and administrators in Ontario colleges. The study leans on the cross-case analysis of three cases comprised of Ontario publicly funded colleges. Fifteen senior leaders (vice-presidents, deans, directors) who are hiring decision makers for faculty and administrators were interviewed. A conceptual framework linking white supremacy to one of its tools, systemic racism (structural, institutional, and individual racism), along with Critical Race Theory and Whiteness Theory, guides the analysis of the data. The findings exposed several barriers in Ontario colleges that affect the hiring of racialized faculty and administrators. Ubiquitous in all three colleges was proceduralism. Hiring policies and procedures and the Faculty Collective Agreement presented as neutral and bias-free are operationalized without critique impact who is in and who is out (who gets hired) and reproduce whiteness in faculty and administration bodies. Furthermore, Culture-fit; meritocracy; scientific racism; implicit bias; the trope of the missing pool of racialized faculty and administrator; and leadership lack of equity and anti-racism knowledge surfaced as blocks to the racial diversification of faculty and administrators. The study presents several recommendations from the researcher and college leaders to advance the hiring racialized faculty and administrators. Keywords: racial diversity in hiring, racial diversity in administration, recruitment and racial diversity, racialized leadership, organizational culture, systemic racism, institutional racism, individual racism, white supremacy, implicit bias, critical race theory, whiteness theory, and organizational theory.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.046 | 0.010 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".