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Record W4298251029

Risque(s) et gouvernementalité. Reconstruction théorique et illustration empirique:les usages du risque dans l’économie du châtiment légal

2006· article· fr· W4298251029 on OpenAlexaboutno aff
Gilles Chantraine, Jean-François Cauchie

Bibliographic record

VenueLillOA (Université de Lille (University Of Lille)) · 2006
Typearticle
Languagefr
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Notre article détaille l’appréhension spécifique de la notion de risque dans le cadre théorique d’inspiration foucaldienne de la littérature sur la gouvernementalité (governmentality studies), et la manière dont cette appréhension spécifique met à l’épreuve certains fondements et interprétations de la sociologie dominante du risque telle qu’elle se déploie notamment à travers le métarécit de la « société du risque » d’Ulrich Beck. Nous soulignerons en quoi la perspective gouvernementaliste offre un cadre d’analyse à la fois plus modeste, plus empirique, et plus diversifié que la théorie de la société du risque. Nous appuierons notre argumentation théorique à l’aide d’une recherche empirique spécifique : la gestion des « risques criminogènes » dans les établissements pénitentiaires canadiens, en insistant sur l’inscription de cette recherche dans le questionnement plus global des transformations macrosociologiques du système pénal et de l’économie punitive dans les sociétés contemporaines. Cet effort de clarification théorique, de comparaison analytique et d’illustration empirique mettra au jour les différentes formes de postures constructivistes et les différents types de projets critiques qui fondent l’analyse sociologique d’un risque particulier, et permettra, in fine, de mieux cerner l’espace de pertinence des analyses d’Ulrich Beck.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.014
GPT teacher head0.216
Teacher spread0.202 · 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 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

Citations0
Published2006
Admission routes1
Has abstractyes

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