Student Equity and Inclusive Education Policy in Ontario: Perspectives of Three High School Principals
Bibliographic record
Abstract
Embracing diversity within schools is a complex endeavour. Data on student achievement \nin Ontario’s urban high schools indicate a disconnect between the expectation of \nequitable and inclusive education as stated in the Ontario Ministry of Education’s (2014) \nvision, Achieving Excellence: A Renewed Vision for Education in Ontario, and the social \nrealities of discriminatory barriers in schools. This study employed semi-structured \ninterviews to obtain the perspectives of 3 urban high school principals on the \nimplementation of policies that support the goal of ensuring equity—identified as a key \ngoal in Achieving Excellence. Findings suggest that the interconnectivity of policies, how \nprincipals translate policy messages, and the character traits associated with leadership \nare factors that muddy the implementation process in urban high schools. It was \nsuggested that policy implementation is not static and occurs in a fluid system consisting \nof individuals with differing lived experiences, beliefs, and intersectional identities. \nEmphasizing the delicate state of Ontario’s current political climate, participants \nproposed the dismantling of tokenism and assumptions placed on principals, the \nincorporation of practical support in professional development, and changing the \npathologizing nature of teacher professional judgment as strategies to improve principal \npolicy implementation.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.050 | 0.016 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".