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Record W3033376716 · doi:10.7202/1069447ar

Les mobilisations à l’épreuve de l’opacité policière en France

2020· article· fr· W3033376716 on OpenAlexvenueno aff
Magda Boutros

Bibliographic record

VenueLien social et Politiques · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Cet article analyse l’impact de l’opacité entourant l’action policière sur les mobilisations qui dénoncent les violences et discriminations policières en France. Sur la base d’une enquête qualitative de trois mobilisations contemporaines, l’article montre que les militants perçoivent l’opacité policière comme un obstacle important à l’élaboration de mobilisations, et qu’ils investissent dans la production de connaissances pour corroborer leurs dénonciations. De nombreux travaux sociologiques ont démontré que les mouvements sociaux participent à la production de connaissances, mais peu ont examiné la manière dont la production de connaissances influence à son tour la définition des causes militantes. En comparant différents modes de production des connaissances, l’enquête révèle que, selon la méthodologie de collecte des données, le matériau empirique collecté et le prisme d’analyse adopté, ceux-ci mettent en évidence différents aspects des pratiques policières, et contribuent ainsi à consolider certains cadrages du problème au détriment d’autres.

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)0.008
metaresearch head score (Gemma)0.013
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 score0.090
Threshold uncertainty score0.178

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.003
Science and technology studies0.0100.012
Scholarly communication0.0080.004
Open science0.0010.006
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.304
GPT teacher head0.497
Teacher spread0.193 · 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

Citations15
Published2020
Admission routes1
Has abstractyes

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