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Record W7070458656

Police Accountability in the United States, Canada, UK, Germany and France

2024· other· en· W7070458656 on OpenAlexaboutno aff

Bibliographic record

VenueFlorence Research (University of Florence) · 2024
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGene expression and cancer classification
Canadian institutionsnot available
FundersU.S. Department of Homeland SecurityU.S. Department of Justice
KeywordsNucleofectionTSG101Gestational periodDiafiltrationHyporeflexiaPretextDysgeusiaDemotionLiquation
DOInot available

Abstract

fetched live from OpenAlex

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 debateon 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." This is the English version of the Report La responsabilizzazione della polizia negli Stati Uniti, Canada, Germania e Francia, Università degli studi di Bari, Bari, 2024.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.298

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.018
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.028
GPT teacher head0.300
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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