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Record W4285231412 · doi:10.7202/1089735ar

Satisfaction envers la police dans les communautés rurales et éloignées

2022· article· fr· W4285231412 on OpenAlexaffvenue
Jean‐Denis David

Bibliographic record

VenueCriminologie · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Nos connaissances scientifiques sur les relations police-communauté en milieux ruraux et éloignés sont limitées. L’article a pour objectif de clarifier ces connaissances en examinant les variations sur le plan de la satisfaction des citoyens envers la police à travers un continuum urbain-rural-éloigné. En utilisant la théorie de la structuration, nous envisageons, dans cette recherche, que le contexte des communautés rurales et éloignées apporte des contraintes et permet des pratiques policières différentes de celles de leurs collègues en milieux urbains, engendrant des variations quant aux perceptions des citoyens de la police. Grâce au recours aux données de l’Enquête sociale générale de 2014, les résultats suggèrent qu’il existe effectivement des variations importantes en fonction du lieu où les gens habitent. Particulièrement, la satisfaction des citoyens en matière de rendement décline avec l’isolement géographique des communautés rurales et éloignées comparativement aux communautés urbaines. Plus encore, la satisfaction des citoyens quant aux compétences interpersonnelles des policiers est plus élevée dans les communautés rurales comparativement aux régions urbaines. Toutefois, elle décline avec l’isolement géographique des communautés éloignées. Ces résultats contribuent à l’information nécessaire au développement de politiques publiques et de pratiques policières ayant le potentiel d’améliorer les relations police-communauté.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.476
GPT teacher head0.463
Teacher spread0.013 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations2
Published2022
Admission routes2
Has abstractyes

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