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Record W2159891239 · doi:10.7202/029808ar

Le traitement correctionnel fondé sur des données probantes : une recension

2009· article· fr· W2159891239 on OpenAlex
Franca Cortoni, Denis Lafortune

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCriminologie · 2009
Typearticle
Languagefr
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceHabilitationArt

Abstract

fetched live from OpenAlex

Contrairement à la perspective « Nothing works » de Martinson (1974), l’approche « What works ? » du traitement correctionnel s’est centrée sur la possibilité que le traitement correctionnel soit efficace dans la réduction de la récidive criminelle des délinquants. Les preuves empiriques examinées dans le présent article corroborent le fait que l’application des principes risque-besoins-réceptivité d’Andrews et Bonta (2006) donnent les bases d’un modèle efficace de réhabilitation. Pourtant, malgré les grands progrès réalisés dans le développement et la mise en oeuvre d’un traitement correctionnel fondé sur des données probantes, il subsiste des sphères qui nécessitent un approfondissement. Dans cet article, trois questions souvent négligées relativement à la réhabilitation du délinquant sont aussi examinées, à savoir la nécessité de prendre en considération les enjeux motivationnels chez les délinquants, l’importance des compétences et attitudes du personnel, et la nécessité de documenter et d’évaluer continuellement les pratiques de réhabilitation.

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.

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), 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 score0.603
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.370
GPT teacher head0.380
Teacher spread0.010 · 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