MétaCan
Menu
Back to cohort
Record W2767940530 · doi:10.7202/1041706ar

Dissuasion conditionnelle, programme d’accès graduel à la conduite et infractions routières

2017· article· fr· W2767940530 on OpenAlexaffvenueabout
Brigitte Poirier, Étienne Blais, Camille Faubert

Bibliographic record

VenueCriminologie · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

S’inspirant de la théorie de la dissuasion conditionnelle, cet article étudie la relation entre les restrictions associées au permis de conduire, les mécanismes de contrôles sociaux formels et informels et les intentions délictueuses au sein d’un échantillon de 392 jeunes détenteurs d’un permis de conduire régulier ou bien d’un permis restreint (probatoire ou d’apprenti). Dans le cadre du programme d’accès graduel à la conduite québécois, les titulaires d’un permis restreint ont entre autres un nombre limité de points d’inaptitude et sont soumis à une tolérance zéro relativement à l’alcool au volant. Les résultats des analyses de régression montrent que les détenteurs d’un permis restreint manifestent moins d’intentions délictuelles que ceux qui ont un permis régulier. Les résultats établissent également que les pairs délinquants ont moins d’influence sur les détenteurs d’un permis restreint comparativement à ceux qui détiennent un permis régulier. Enfin, l’internalisation de la norme apparaît comme le mécanisme qui prévient le plus efficacement les intentions délictuelles parmi tous les conducteurs.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.289
Threshold uncertainty score0.575

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.609
GPT teacher head0.513
Teacher spread0.096 · 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

Citations0
Published2017
Admission routes3
Has abstractyes

Explore more

Same venueCriminologieSame topicCrime Patterns and InterventionsFrench-language works237,207