An Examination of Educational Leadership Preparation in Ontario: Are Principals Prepared to Lead Equitably?
Bibliographic record
Abstract
In response to the changing demographics of schools in Canada and efforts to better equip principals to challenging inequity, leadership preparation programs have adopted new policies focused more on leading with an equity lens. However, studies have demonstrated a disconnect between what is covered in these leadership programs and how school principals actually perceive their ability to lead equitably and work with diverse learners. Six current school principals and vice principals in Ontario, Canada who have successfully completed a Principal Qualification Program (PQP) course were interviewed to understand their perceptions on the program’s ability to prepare them to lead, and their perceptions on concepts of equity, diversity, and inclusion (EDI). The racial experiences and identities of each participant shaped their definitions of EDI, as well as their understandings of difference. Study findings indicate several critical areas of change for principal preparation programs in Ontario: training guidelines, efforts to prepare educators to be equitable leaders, and the educators’ perceptions on their preparedness to lead. Utilization of Critical Race Theory in Education and Applied Critical Leadership additionally help frame analysis and support the need to integrate culturally relevant pedagogical practice into leadership preparation programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".