Keeping the Peace: How Equity is Leveraged through Safe Schools Policy
Bibliographic record
Abstract
This paper describes research which examines the implementation at the school level of a safe schools policy which has elements of both anti-discrimination education and peacekeeping.In the process, both the formal text of this policy and its school-level implementation are considered.The authors present a brief history of safe school policies in Ontario, Canada, juxtaposing this with the concurrent development of equity and anti-discrimination policies in the same jurisdiction.The study's research questions ask how school administrators are responding to safe schools policies which are intended to build more inclusive schools.The participants are secondary school vice principals in several district school boards.The findings indicate that school leaders enact safe schools policies with intentions of fairness without necessarily referencing recent or longstanding policies for either safe schools or equity.The study also finds some evidence of educating students informally toward more respectful, anti-discriminatory practices.The researchers conclude that in spite of new policy which includes more progressive and restorative approaches to safe schools, policy enactment in some Ontario schools may still be more reflective of a process of keeping the peace rather than teaching anti-discrimination.The authors speculate that the provincial safe schools policy could be used as one tool to leverage equity initiatives.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".