La télésurveillance policière dans les lieux publics : l'apprentissage d'une technologie
Bibliographic record
Abstract
Few studies have investigated law enforcement agencies' motivation and capacity to integrate video surveillance of public places into patrol- and criminal-investigation practices or the extent to which that motivation and capacity are constrained by independent regulatory agencies. In this paper, we assess the impact of a law-enforcement experiments in video surveillance in Montreal during a five-year period (2004 to 2008). Two strategies are compared. The first strategy made use of CCTV as a proactive and integrated element of a problem-solving initiative targeting an open-air drug-dealing market. The second strategy was essentially passive and CCTV cameras were spread along a street known for its nightlife, bars scene, and clubs. Findings show that video surveillance is, in fact, effective when closely linked to traditional police strategies and focused on a specific, recurrent, and localized problem. CCTV did manage to have an impact on the incidence of drug-dealing transactions as well as a collateral impact on the incidence of other offences, especially violent crimes. The second and more common approach, however, had no impact on crime.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".