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Record W2469539050 · doi:10.7202/1036200ar

Classes dominantes, classes délinquantes ?

2016· article· fr· W2469539050 on OpenAlexaffvenue
Jean Bérard

Bibliographic record

VenueCriminologie · 2016
Typearticle
Languagefr
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversité de MontréalInternational Centre for Comparative Criminology
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’article aborde la question de la délinquance des élites en déplaçant l’attention des travaux criminologiques vers les travaux des économistes et sociologues critiques, principalement français, qui portent sur les comportements des classes dominantes. La problématique porte sur les analyses formulées pour rendre compte de la crise actuelle en incriminant le comportement des classes dominantes. L’article étudie la mobilisation du vocabulaire de la déviance pour qualifier des comportements économiques et politiques de vol, d’extorsion et de violence. Il montre que ces dénonciations s’appuient sur des analyses de ces comportements comme un trait majeur des pratiques des classes dominantes contemporaines. En particulier, le renouveau de la sociologie critique s’appuie sur l’analyse du rapport singulier aux lois et aux normes des classes dominantes, en montrant qu’un de leurs traits distinctifs est de considérer ces règles comme fondamentales pour les autres, mais contournables par elles-mêmes. De telles analyses conduisent à des propositions politiques qui font usage de l’idée de sanction pénale. Mais l’article montre que ces usages sont pris dans des enjeux politiques plus larges et, en particulier, replacés dans le jeu des rapports de force entre les États, et entre les États et les détenteurs de capitaux qui échappent à leur contrôle.

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)0.004
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 score0.057
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0060.009
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.332
GPT teacher head0.373
Teacher spread0.041 · 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

Citations3
Published2016
Admission routes2
Has abstractyes

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