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Record W2618338502 · doi:10.7202/1039804ar

Liens drogue-délinquance lucrative chez les adolescents

2017· article· fr· W2618338502 on OpenAlexaffvenue
Elisabeth Lacharité-Young, Natacha Brunelle, Michel Rousseau, Iris Bourgault Bouthillier, Danielle Leclerc, Marie‐Marthe Cousineau, Joël Tremblay, Magali Dufour

Bibliographic record

VenueCriminologie · 2017
Typearticle
Languagefr
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversité de SherbrookeUniversité de MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

L’adolescence est souvent le berceau de plusieurs conduites déviantes cooccurrentes. La consommation de substances psychoactives (SPA) et la délinquance figurent parmi ces conduites et entretiennent des liens multiples et parfois complexes. Cette étude vise à explorer le modèle drogue-délinquance économico-compulsif auprès des jeunes. Plus précisément, elle a pour but de : 1) dresser un portrait des habitudes de consommation de SPA et de la délinquance lucrative des jeunes de l’échantillon ; 2) documenter la relation entre la gravité de la consommation de SPA et la commission de délits lucratifs ; ainsi que 3) celle entre le type de SPA consommées et la commission de délits lucratifs ; et de 4) vérifier l’interaction entre le type de SPA consommées et le genre dans la prédiction de la commission de délits lucratifs. Un instrument de mesure sur la gravité des habitudes de consommation de SPA (DEP-ADO) et un autre sur la délinquance (MASPAQ) des adolescents ont été administrés à 1447 jeunes âgés de 15 à 18 ans. Les résultats permettent d’observer en partie le modèle explicatif économico-compulsif chez les jeunes tout en lui apportant certaines nuances.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.429
GPT teacher head0.423
Teacher spread0.006 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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