Constructing bullying in Ontario, Canada: a critical policy analysis
Bibliographic record
Abstract
As the prevalence and negative effects of bullying become widely known, people around the world seem desperate to solve the bullying “problem”. A sizeable body of research about many aspects of bullying and a plethora of anti-bullying programmes and policies now exist. This critical policy analysis asks: how does Ontario, Canada’s bullying policy support and/or undermine critical democracy; and how does it reflect, support and further the interests of neoliberalism and/or neoconservatism? Findings indicate that the policy constructs the problem of bullying as a problem of individuals and a “behaviour for learning” problem. The policy also prescribes standardised responses to bullying incidents. We explore ways in which these constructions are undemocratic and unjust. The findings are particularly concerning because bullying policies are often viewed as innocuous by practitioners. This paper offers more than just critique by providing suggestions for how research and policies can become more just and equitable and how bullying policy may be enacted to support critical democracy.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".