BULLYING, INSTITUTIONS AND THE THERAPEUTIC CULTURE BULLYING, INSTITUTIONS AND THE THERAPEUTIC CULTURE: HOW SCHOOLS RESPOND TO SOCIAL PROBLEMS
Bibliographic record
Abstract
A.hstract: Based on empirical interview evidence and data published by the Ontario Ministry of Education, this thesis uses Institutional Theory, and notions of the Therapeutic Culture to examine how and why bullying is addressed within schools. This research analyzes the claims produced and disseminated through the Ministry of Education's written policies and procedures, and the actual responses and views of school administrators. Two data sources are used: documents from the Ontario Ministry of Education, and twenty-two interviews with princilpals, teachers, and other key players within schools in Hamilton, Ontario. Interviews suggest that the Ministry, as well as the boards and individual schools, actively construct myths regarding anti-violence, and loosely couple their practices with their policies in ways that placate members of their institutional environment and allow them to retain organizational legitimacy. These results support Institutional Theory, suggesting that these anti-violence policies and procedures are ceremonial, serving as "band-aid " solutions, rather than resolving the problems of bullying and violence within schools. Furthermore, they demonstrate the impact of the Therapeutic Culture as the new cultural frame that schools utilize as a
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".