Mesure des risques de victimisation associés à la pratique clinique auprès des détenus fédéraux
Bibliographic record
Abstract
This article focuses on three questions related to the risks of criminal victimization encountered by clinicians in criminology: 1) what are the general incidence, prevalence and forms of victimization in this profession? 2) Does victimization occur mostly in work settings or during any other routine activity? 3) Are these events of victimization stable across time or within the individual's career? Statistically, two main patterns emerge: violent incidents occur in works settings (mostly assaults, assaults with a weapon, illegal confinements) while non-violent events happen outside of occupational settings. In the case of our respondents, between 1976 and 1990, our results show that the risks of criminal victimization were increased by a factor of 33% merely by being clinicians in the field of criminology. Theoretical explanations are drawn from the routine activity model (Cohen and Felson, 1979) and the construction process of social problems (Spector and Kitsuse, 1977).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".