Policer la violence: analyse du taux de déclaration et du taux d’enregistrement des victimisations criminelles au Canada en 1999 et 2004
Bibliographic record
Abstract
This article uses data from Canadian victimization surveys between 1999 and 2004 to analyse the factors shaping two decisions of vital importance to the justice system – namely, the reporting of a crime to the police and the recording of a crime by the police. These analyses serve to show that the seriousness of the crime, and not the characteristics of the victim, is the main factor driving the decision to report a crime to the police. Likewise, the police officially record approximately 80% of the criminal events reported to them and are not influenced by the characteristics of the victim (e.g., age, gender, ethnic origin, schooling level, income, etc.). For this reason, they appear to take a protocol-based approach to their work. Finally, the analyses show that the reporting and recording rates for crimes against property were lower in 2004 than in 1999 – a fact that may partially account for the drop in Canada's official crime rate. However, the reporting and recording rate for violent crimes is up. Accordingly, violent crime has become a growing focus of concern among the general public and the police authorities alike.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".