Du côté des victimes, une autre perspective sur le vol à main armée
Bibliographic record
Abstract
Armed robbery has long been regarded as a crime against property. But from the victim's point of view, it is a violent crime which endangers their lives and constitutes a traumatic experience. Nine years of research are briefly summarized with special attention to a recent survey of victims of commercial robbery in Montreal in which 440 persons were interviewed. It is difficult to describe a victim unless researchers agree on some basic definition of who should be defined as a victim. This is the first subject of discussion. After a short description of the way victimizations occur, the consequences of the robbery are discussed, and the responses of the mental health and justice systems are presented. Most victims do not resist and those who do so seem to be reacting to past victimizations or to an excess of violence on the part of the robber. Nearly 90 % of victims suffer some kind of emotional trauma and far from being helped in this regard, this trauma is often aggravated by the criminal justice system's response. It seems to affect the victim much more than the financial, physical and social consequences of the crime, which had little effect on their attitudes and needs. The main problem with armed robbery is that it creates and perpetuates a climate of suspicion, fear and anger very damaging to social relationships. These negative effects can be reduced, however, and the study points out some of the means by which this can be accomplished.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".