10. What Are the Critical Elements in Safe School Legislation to Prevent Bullying?
Bibliographic record
Abstract
Research shows that effective school policies mean less bullying and a better school climate, but there is a limited understanding of the connection between evidence-based policy and the prevalence of bullying and victimization. The goal of this research is to determine if regional differences in policy and legislation are associated with the prevalence of bullying in Canada and the United States. Does policy predict differences at the regional level, as well as between countries? What are the critical elements within these policies and legislations that predict bullying and victimization? This study used archival data from the Canadian sample of the 2013/2014 Health Behavior in School-Aged Children (HBSC) survey. 30,153 students from across Canada participated. Each legislation was coded based on a checklist reflecting best practices in the literature. High scores reflected a large quantity of evidence based policy items. Mplus 7.3 was used for multilevel modeling, with MLR estimator to account for the non-normality of the bullying variables. This study found there were national and regional differences in the evidence-base of bullying policies. There was a negative association between the comprehensiveness of a region’s anti-bullying policy and the prevalence of bullying behaviour in that region. There is a negative relationship between the number of evidence based policy items in Canadian legislation and the prevalence of bullying and victimization. Further research is needed to explore the relationship between evidence-based policies and the prevalence of bullying and victimization over time.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".