The School Ressource Officer and the Effectivity of Law. Comparative Analysis of Police’s Teaching Work About Law in France and in Canada
Bibliographic record
Abstract
Starting in the 1980s in Canada and the 1990s in France, police started to implement teaching curricula in schools, as part of a risk management and crime reduction program targeting juveniles. This article examines the ways in which the police interpreted and applied their new teaching mandate, and how they developed their teaching heuristics. As a study in the sociology of policing, this article compares the very different ways in which French and Canadian police made sense of their new responsibilities. The article contends that these pedagogical interactions with students help shape the very different ways in which the police in the two countries construct their citizens’ relationship with the law and with its application to real-world situations. The article explores the reasons why the police of France and Canada take such different approaches to their efforts to make the legal order appealing to their young charges.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".