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Record W4394889164 · doi:10.1515/9782760645264

Profilages policiers

2022· book· fr· W4394889164 on OpenAlexaboutno aff
Pascale Dufour, Francis Dupuis‐Déri

Bibliographic record

VenueLes Presses de l'Université de Montréal eBooks · 2022
Typebook
Languagefr
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Cet ouvrage présente les résultats de neuf enquêtes sur le profilage policier menées par des spécialistes qui se sont penchés sur les pratiques de corps policiers au Québec, en France et en Argentine. Il met en lumière les dynamiques de croisement entre différents types de profilages (racial, social, politique et de genre, entre autres) et se démarque en cela de l'habitude d'étudier ce phénomène en fonction d'une seule caractéristique. Tout en exposant les particularités de chaque profilage, il ouvre ainsi la voie aux études intersectionnelles sur le sujet. D'une brûlante actualité, et très pertinent d'un point de vue des sciences sociales, ce recueil intéressera toutes les personnes préoccupées par les inégalités croissantes dans nos sociétés ainsi que par la violence subie par les populations discriminées, stigmatisées et marginalisées ciblées par la police. -- quatrième de couverture

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.177
Threshold uncertainty score0.351

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.003
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0400.008

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 head0.040
GPT teacher head0.316
Teacher spread0.275 · 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 designNot applicable
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

Citations5
Published2022
Admission routes1
Has abstractyes

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