MétaCan
Menu
Back to cohort
Record W2106054072 · doi:10.1177/1541204012458440

Onset, Offending Trajectories, and Crime Specialization in Violence

2012· article· en· W2106054072 on OpenAlex
Stacy Tzoumakis, Patrick Lussier, Marc Le Blanc, Garth Davies

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueYouth Violence and Juvenile Justice · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversité LavalUniversité de MontréalSimon Fraser University
Fundersnot available
KeywordsInjury preventionPsychologyHuman factors and ergonomicsPoison controlLongitudinal studyViolent crimeLongitudinal dataSuicide preventionCriminologyDemographyMedical emergencyMedicineSociology

Abstract

fetched live from OpenAlex

Using data from the Montreal Longitudinal Study, the current study investigates whether age of onset is informative about the dynamic aspects of violent behaviors in males over time, in terms of violent offending frequency, crime trajectory, and, most importantly, crime specialization in violence. Self-reported data at three time points were used. Group-based modeling showed much heterogeneity in the shape of violent trajectories, which were associated with various crime specialization patterns over time. Most importantly, the number and shape of these trajectories were not accounted for by overall age of onset. Study findings show that while age of onset, especially the age of onset of violence, might be informative of the likelihood of committing a violent crime in middle adolescence, it is not informative about the dynamic process of violent offending. Of importance, violent adult offenders specializing in such crimes in adulthood were not necessarily early starters.

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.

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)0.001
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 score0.119
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.053
GPT teacher head0.337
Teacher spread0.284 · 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