Making a difference in bullying: evaluation of a systemic school-based programme in Canada
Bibliographic record
Abstract
Impetus for the intervention study, early stages of planning, and funding Over the past decade, Canadians have become increasingly aware of the extent and consequences of bullying problems. Recently, there have been several high-profile cases of Canadian children who have suffered from prolonged victimisation, with severe consequences of suicide, revenge attacks, or death at the hands of peers. These cases have highlighted the need for empirically based prevention and intervention programmes. We will describe a school-based intervention programme developed prior to the recent surge in interest in the problem of bullying in Canada. This anti-bullying initiative emerged from a survey conducted in the early 1990s by the Toronto Board of Education in collaboration with researchers from York University. The questionnaire used for the survey was modelled after the Olweus self-report questionnaire (Olweus, 1989), with some adaptations for the Canadian context. The survey indicated that bullying and victimisation were pervasive problems. During the past two months, 24% of the grade 3–8 students reported that they had bullied other students at least once or twice, and 15% more than once or twice. Half of the students (49%) indicated that they had been victims of bullying at least once, 20% more than once or twice, and 8% reported being victimised weekly or more often during the past two months (Charach, Pepler, and Ziegler, 1995).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".