Preventing School Bullying: Identification and Intervention Strategies Involving Bystanders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it