Preventing School Bullying: Identification and Intervention Strategies Involving Bystanders
Bibliographic record
Abstract
An effective prevention strategy must start with a clear understanding of the behaviour involved; in the case of bullying some of the latest research provides a good starting point. Recent studies have demonstrated that bullying or "the abuse of physical and psychological power for the purpose of intentionally and repeatedly creating a negative atmosphere of severe anxiety, intimidation and chronic fear in victims" (Marini, Spear & Bombay, 1999, p. 33) is one of the most pervasive and serious socioeducational problems facing students (Juvonen & Graham, 2001; Olweus, 2001; Rigby, 2002). Studies suggest that bullying behaviours have a much earlier onset than previously thought, the number of students affected is rather high (10% to 15% report being often victimized), the range of behaviours involved can be quite severe, and the consequences are long lasting (Craig, Pepler & Atlas, 2000; Kochenderfer & Ladd, 1996; Loeber & Hay, 1997; Olweus, 2001; Smith, Cowie, Olafsson & Liefooghe, 2002). With the acknowledgement that bullying is "commonplace" in school settings and a serious problem for many students, issues related to possible prevention, have become the focus of attention.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".