2015 – From United States to France : it’s time to trace à “big picture” for the private security
Bibliographic record
Abstract
Comme chaque année, la sécurité privée était à l’honneur lors du congrès de l’International Association of Security and Investigative Regulators (IASIR), organisé en novembre à la Nouvelle Orléans. Occasion pour Cédric Paulin, adjoint du directeur de cabinet au Conseil national des activités privées de sécurité, de dresser une synthèse des thèmes qui animent les régulateurs de la sécurité privée outre-Atlantique : le rôle de la sécurité privée en matière de gestion de crise, et notamment de catastrophes naturelles, les évolutions du métier des détectives privés confrontés au défi de l’« uberisation ». L’auteur en tire des enseignements pour le métier en général mais aussi pour l’univers de la sécurité privée hexagonale. Parmi ces enseignements, le rôle du transfert d’informations dans l’inéluctable coproduction de sécurité. 2015, nous rappelle Cédric Paulin, a vu la France entamer son rattrapage en matière de normalisation dans la sécurité privée. Cette prise en compte de la normalisation constitue une étape importante de la coproduction de sécurité.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".