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Record W2152108169 · doi:10.7202/1005303ar

Quand drogues et violence se rencontrent chez les jeunes : un cocktail explosif ?

2011· article· fr· W2152108169 on OpenAlexaffvenueabout
Serge Brochu, Marie‐Marthe Cousineau, Chloé Provost, Patricia Erıckson, Sun Fu

Bibliographic record

VenueDrogues santé et société · 2011
Typearticle
Languagefr
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsInternational Centre for Comparative CriminologyCentre for Addiction and Mental HealthUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyEthnologyPhilosophy

Abstract

fetched live from OpenAlex

Les jeunes qui se retrouvent en centre de réadaptation pour jeunes contrevenants constituent un groupe qui mérite une attention particulière lorsqu’il s’agit de faire le point sur les relations qui se nouent entre alcool/drogues et violence. Cet article a pour but de décrire les liens qui se tissent entre substances psychoactives (entendre alcool et drogues illégales) et violence chez les jeunes contrevenants. Plus spécifiquement, il s’agit d’exposer le rôle : a) des intoxications; b) du besoin d’argent pour se procurer des drogues ; et c) du système de distribution illicite des drogues dans la manifestation de comportements violents chez les jeunes contrevenants canadiens. Les données traitées dans cet article sont issues d’un questionnaire adressé aux jeunes contrevenants de sexe masculin admis dans les centres de réadaptation du Québec (n = 239) et de l’Ontario (n = 162) quel que soit le délit à l’origine de leur prise en charge institutionnelle. Parmi les trois types de relation étudiés, c’est l’intoxication qui se révèle le facteur le plus important menant à la violence. Notons par ailleurs qu’une bonne partie des crimes associés aux substances psychoactives le sont à plus d’un titre. Des pistes d’interprétation sont suggérées.

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)0.002
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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.458
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0260.001

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 head0.186
GPT teacher head0.397
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations8
Published2011
Admission routes3
Has abstractyes

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