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Record W2115749054 · doi:10.7202/017241ar

La carrière criminelle : définition et prédiction

2005· article· en· W2115749054 on OpenAlexaffvenueabout
Marc Leblanc

Bibliographic record

VenueCriminologie · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsInternational Centre for Comparative Criminology
Fundersnot available
KeywordsPredictabilityDictionCriminologyPsychologyVariety (cybernetics)Degree (music)SociologyPolitical sciencePhilosophyMathematicsArtificial intelligenceComputer scienceLinguistics

Abstract

fetched live from OpenAlex

The concept of a criminal «career» is being used more and more in the recent criminological literature. This article analyzes the pertinence of explaining this concept by its specific components. To do this, the criminal activities of a sample of adolescents and a group of wards of the Montreal Court between the ages of seven and twenty-five are described (precocity, frequency, variety, gravity, aggravation, violence, duration). Two stages were detected in the criminal career, their degree of stability and paths of development. The dynamics of the criminal activity are described. Finally, the predictability of an intensification of criminal activities is analyzed. Given the high degree of stability, predictability and mobility (marked by aggravation of the criminal activity), we conclude that recourse to the concept of a criminal career is essential to a better understanding and more accurate diagnosis of the process of acting out.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.904
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.514
GPT teacher head0.494
Teacher spread0.021 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations10
Published2005
Admission routes3
Has abstractyes

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