Risques sous surveillance : une analyse géographique de l’utilisation de la vidéosurveillance
Bibliographic record
Abstract
Gérer des risques implique la mise au point de dispositifs de surveillance capables de donner des indications précises sur la situation à gérer, de manière à pouvoir décider de l’intervention la plus adéquate à fournir. Cet article interroge d’un point de vue géographique la vidéosurveillance, dispositif de surveillance doté de caméra dont la pratique est en train de s’étendre, et propose une réflexion sur les transformations des espaces urbains publics concernés qu’il est possible d’observer. Si les caractéristiques des espaces surveillés vont être déterminantes dans le type de vidéosurveillance qui sera pratiqué – en fonction des catégories de risques qui y sont identifiées –, cette dernière joue à son tour un rôle important dans le processus de transformation des espaces publics, notamment en augmentant la part de la sécurité privée dans le domaine public.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".