Governing bullying through the new public health model: a Foucaultian analysis of a school anti-bullying programme
Bibliographic record
Abstract
Framed as a public health problem, school bullying led public health agencies to design anti-bullying programmes. The public health approach is invested with hope by those who are looking for an alternative to the punitive logic. Using a Foucaultian approach and a discourse analysis method, this research focuses on the way an anti-bullying intervention programme designed by a public health agency governs school bullying. The findings reveal two major logics at play. Firstly, the programme espouses the new public health model and, accordingly, governs bullying as a systemic risk rather than an individual problem. Secondly, the programme is also anchored in the classical punitive rationality. Public health and punitive logics, far from being mutually exclusive, are rather intertwined. This dual logic contributes to the ‘dangerization’ of school bullying.
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How this classification was reachedexpand
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".