Countering school bullying: An analysis of policy content in Ontario and Saskatchewan
Bibliographic record
Abstract
The incidence of extreme school violence as a direct consequence of bullying among peers, exacerbated by vast media attention, has caused educational institutions worldwide to put bullying intervention and prevention strategies into operation. This study focused on an overview of two provincewide antibullying incentives in the Canadian provinces of Ontario and Saskatchewan, and an analysis of the quality of their respective antibullying policies. An itemized list of beneficial practices for bullying intervention and prevention originated from Smith, Smith, Osborn and Samara (2008)’s scoring scheme. The scoring scheme was adapted to the current study by linking research-based program elements that have been found to be effective in reducing school bullying to a content analysis of both provincial frameworks. The final scoring scheme comprised a total of 39 criterions, divided into five categories: Defining Bullying Behaviors, Establishing a Positive School Climate, Disseminating, Monitoring and Reviewing Policy, Reporting and Responding to Bullying, and Involving the Broader Community. Results showed that policies contained a total average of 60% of the criterions in Ontario, and 59% in Saskatchewan. The conclusion of this study observes from policy lenses key essentials of bullying intervention and prevention initiatives in elementary and secondary educational settings. Recommendations are proposed to bridge the gap between areas that have received extensive attention and areas that have received less treatment in bullying intervention and prevention endeavors, using the content of Ontario and Saskatchewan policies as a basis for discussion.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 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".