A la recerca d'un model publicoprivat de gestió de la seguretat
Bibliographic record
Abstract
Durant les darreres decades hi ha hagut grans transformacions a les institucions publiques, tradicionalment gestionades a traves de l’Estat. Dins d’aquestes institucions, la policia ha estat l’agencia de control social que mes ha evolucionat, mentre que la seguretat privada i el seu ascens ha estat un dels factors que mes ha contribuit a aquesta transformacio. El rapid desenvolupament de les empreses de seguretat, tant a Espanya com a d’altres paisos, ha transformat la gestio de la seguretat en els darrers anys, ja que aquestes empreses de seguretat no han crescut de manera paral·lela annexionant-se als serveis policials existents, sino que s’han desenvolupat en molts casos superposant-se a l’ambit, les funcions, les competencies i les responsabilitats que abans pertanyien a la seguretat publica (Shearing i Stenning 1983a, 1987b; Johnston 1992, Marx 1987, Cunningham i Taylor 1985). A Espanya hem passat en els ultims vint anys de 925 empreses de seguretat el 1986 a 1.034 empreses de seguretat el 2003. I de 31.000 vigilants de seguretat a 103.6991 en les mateixes dates, segons les dades del Ministeri de l’Interior. No arribem a taxes del triple de vigilants de seguretat per policies, com a Sud-africa, o el doble com als Estats Units i al Canada, sino que a Europa la mitjana segueix estant en la meitat de vigilants per policies (De Waard 1999). Aquesta transformacio ha estat qualificada per Shearing i Stenning (1983, 17) de revolucio silenciosa (Quiet Revolution) ja que, a diferencia de les transformacions en materia de seguretat (canvis legislatius, politics, etc.) que hi va haver en el segle XIX, com l’estatalitzacio de la seguretat i la creacio de les policies modernes, aquesta evolucio actual en sentit invers esta passant, a la majoria de paisos, sense A la recerca d’un model publicoprivat de gestio de la seguretat
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 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".