A case study of two schools use of bullying prevention strategies after a systematic implementation led by a research team
Bibliographic record
Abstract
There is little understanding of how bullying prevention programs penetrate the school culture over time, or how they may be adapted into the organizational social system. The proposed research seeks to understand how bullying prevention is enacted within a school that had previously experienced an intensive bullying prevention program implementation. The proposed study will be approached through the lens of complexity theory with concepts from general systems theory. This theoretical framework permits an examination of the organizational learning and change that has occurred since the implementation of the bullying prevention program. The proposed study will use a three phase participatory, mixed-method case study approach with two Canadian schools. The data collection will include a quantitative survey, individual interviews, and elaborative focus groups. Throughout the research process, participants will be encouraged to review and contribute to research tool development and analyses through the use of a dedicated research website. The findings may contribute to a better understanding of school’s response to and enactment of bullying prevention programs and provide practitioners and researchers with new factors to consider when implementing bullying prevention programs. Additionally, the proposed research may provide best practices for supporting schools in long-term program sustainability of prevention programs.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".