Bibliographic record
Abstract
The Teacher Racial Diversity Gap in OntarioDonna Hilary Donalds Doctor of Philosophy Department of Leadership, Higher and Adult Education University of Toronto 2025 Abstract Extensive research in Ontario emphasizes the critical importance of equitable hiring; particularly the intentional recruitment of racially diverse teachers to better reflect and serve the province’s increasingly diverse student population. While Ontario has established a range of policies, and guidelines to support this goal, educational researchers have consistently found a persistent disconnect between these efforts and actual outcomes. Despite growing diversity among students and heightened attention to equitable hiring practices, the racial composition of the teaching workforce in Ontario has remained largely homogenous. This enduring gap signals the need for a deeper examination of the teacher recruitment and hiring process, particularly the role of school principals. This study investigates how principals interpret and implement equitable hiring policies and explores the extent to which their decision-making is shaped by their understanding, beliefs, and attitudes toward racial diversity in education. By analyzing how principals make sense of and act upon equity-focused hiring ii guidelines, the research assesses whether these policies are driving meaningful change or merely serving as performative responses to calls for greater representation. The findings highlight the pivotal role of equity-minded leadership in transforming hiring practices. They also point to the necessity of aligning policy with practice and building the capacity of school leaders to enact equity in tangible, measurable ways. This research contributes to the broader discourse on systemic change in education and reinforces the urgency of addressing structural barriers to teacher racial diversity in Ontario. Keywords: Teacher Racial Diversity Gap, Teacher Hiring Practices, Principal Decision-Making, Critical Race Theory in Education, Equity, Representation
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".