Race(ing) to the Top: Interrogating the Underrepresentation of BIPOC Education Leaders in Ontario Public Schools
Bibliographic record
Abstract
Although there have been many calls to diversify the Ontario teacher workforce there has not been the same attention toward troubling the administrator diversity gap within publicly-funded education and its impacts on teacher hiring.Extant literature suggests that those responsible for making hiring decisions often hire candidates that resemble their own positionality.This conceptual paper is concerned with interrogating the administrator diversity gap and its impact on hiring within Ontario's publiclyfunded education system through an Applied Critical Leadership (ACL) theoretical lens.The paper will explore the current context of administrative demographics in Ontario, hiring policies that have contributed to the lack of BIPOC (Black Indigenous People of Colour) educators in permanent teaching and leadership positions, and gatekeeping mechanisms that hinder BIPOC candidates from accessing permanent teaching and leadership positions.This paper further contends that equitable hiring practices and representation cannot materialize without administrators engaging in transformative, critical self-reflective practice.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".