La responsabilizzazione della polizia negli Stati Uniti, Canada, Regno Unito, Germania e Francia
Bibliographic record
Abstract
Questo volume si propone come strumento utile per la ricerca, per i policy-makers, per la formazione, ma anche per coloro che operano in ambito giuridico, così come per la società civile, con l’obiettivo di stimolare il dibattito e il confronto sul tema della responsabilizzazione delle forze dell’ordine. Esso intreccia dati legislativi, analisi giurisprudenziali, studi accademici e contributi della stampa e delle organizzazioni della società civile. Esamina i diversi meccanismi di responsabilizzazione delle forze di polizia attraverso l’analisi delle normative, delle politiche interne e delle prassi operative, mettendo in luce le problematiche e i punti di forza di ciascun sistema. La ricerca raccolta in questo volume è stata coordinata e curata da Lucia Re, responsabile dell’unità di ricerca dell’Università di Firenze nel progetto PRIN PNRR 2022 “Repolity- Reforming Police Accountability in Italy”. This volume is intended as a useful tool for research, policy-makers, education, actors in the legal field, as well as civil society, with the aim of stimulating debate on the topic of law enforcement accountability. It weaves together legislative data, case-law analysis, academic studies and contributions from the press and civil society organizations. It examines the various mechanisms of police accountability through analysis of legislation, internal policies and operational practices, highlighting the problems and strengths of each system. The research collected in this volume was coordinated and edited by Lucia Re, head of the research unit at the University of Florence in the PRIN PNRR project "Repolity - Reforming Police Accountability in Italy”.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 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".