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Record W2803182533 · doi:10.4000/books.pum.6604

9. Quelle est l’efficacité des traitements des agresseurs sexuels pour prévenir la récidive ?

2010· book-chapter· fr· W2803182533 on OpenAlexaboutno aff
Franca Cortoni, Nicholas Longpré

Bibliographic record

VenuePresses de l’Université de Montréal eBooks · 2010
Typebook-chapter
Languagefr
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsGynecologyPolitical scienceHumanitiesMedicinePhilosophy

Abstract

fetched live from OpenAlex

La délinquance sexuelle est un problème particulièrement troublant pour la société. Au Québec, on estime respectivement à 125 et 252 sur 100 000 la proportion des femmes et des enfants qui ont été victimes d’agression sexuelle (Sécurité publique Québec). Grâce à la recherche, on connaît maintenant mieux les facteurs associés à la délinquance sexuelle. De plus, on comprend que l’évaluation du risque est intimement liée à des pratiques efficaces dans le traitement et la prise en charge des agresseurs sexuels. Ce chapitre fait l’état des connaissances sur les facteurs de risque de la récidive des agressions sexuelles. L’accent est mis sur les aspects qui améliorent l’efficacité des thérapies mises en œuvre.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.251
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.005

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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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 designSystematic review
Domainnot available
GenreReview

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
Published2010
Admission routes1
Has abstractyes

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