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Record W1991260679 · doi:10.1177/0093854810379702

Criminal Trajectories and Risk Factors in a Canadian Sample of Offenders

2010· article· en· W1991260679 on OpenAlexaffabout
Ashley K. Ward, David M. Day, Irene Bevc, Ye Sun, Jeffrey S. Rosenthal, Thierry Duchesne

Bibliographic record

VenueCriminal Justice and Behavior · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversité LavalUniversity of TorontoUniversity Health NetworkToronto Metropolitan University
Fundersnot available
KeywordsInjury preventionPoison controlMultinomial logistic regressionPsychologySuicide preventionHuman factors and ergonomicsOccupational safety and healthPeer groupDemographySample (material)Developmental psychologyMedicineMedical emergencyStatistics

Abstract

fetched live from OpenAlex

This study contributed to the criminal trajectory literature using a Canadian-based sample of offenders and examined childhood and adolescent predictors of trajectory group membership. The sample comprised 378 males who had been sentenced as youth, between 1986 and 1996, to one of two open custody facilities in Toronto, Canada. Official criminal records were obtained from late childhood and early adolescence into adulthood for an average follow-up of 12.1 years. Childhood and adolescent predictors reflecting individual, family, peer, and school domains were extracted from client files. Trajectory analysis yielded four groups, labeled moderate rate (MR); low rate (LR); high-rate, adult peaked (HRADL); and high-rate, adolescence peaked (HRADOL). Multinomial regression analyses indicated that risk factors representing the family and peer domains differentiated the MR, HRADL, and HRADOL groups from the LR group. Moreover, whereas both child and adolescent risk factors were associated with the MR, HRADL, and HRADOL groups, only adolescent risk factors were associated with the LR group.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.469

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.402
Teacher spread0.317 · 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.

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

Citations44
Published2010
Admission routes2
Has abstractyes

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