Revisiting the whole-school approach to bullying: Really looking at the whole school
Bibliographic record
Abstract
The whole-school approach to bullying prevention is predicated on the assumption that bullying is a systemic problem, and, by implication, that intervention must be directed at the entire school context rather than just at individual bullies and victims. Unfortunately, recent meta-analyses that have looked at various bullying programs from many countries have revealed that whole-school interventions designed to combat bullying have had limited success in reducing bullying. The purpose of the present study was to establish more clearly the precise aspects of school climate that are linked specifically to the problem of bullying. We used hierarchical linear modeling (HLM) to analyse school-level effects in a data set consisting of 18,222 students from across France. For physical and verbal/relational bullying, the final models respectively explain 6% and 16% of the within-school variance, and 48% and 9% of the between-school variance, significant between-school effects, with the climate variables of school security and the quality of student-teacher relationships emerging as the strongest predictors.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".