Links between trajectories of self‐reported violent and nonviolent offending and official offending during adolescence and adulthood
Bibliographic record
Abstract
BACKGROUND: Little is known about the associations between self-reported offending and official offending whilst considering different types of offences. AIMS: The aims of the present study are to identify developmental trajectories of self-reported violent and nonviolent offending (SRVO; SRNVO) and to examine their associations with official violent and nonviolent offences (as juveniles and adults). METHODS: Developmental trajectories of SRVO and SRNVO from 11 to 17 years of age were estimated with data from the Montreal Longitudinal and Experimental Study, a prospective longitudinal study of 1037 boys from disadvantaged neighbourhoods. RESULTS: Five trajectories of SRVO (i.e. Chronic, Desisting, Delayed, Moderate and Low) and three trajectories of SRNVO (Chronic, Moderate and Low) were identified. Chronic, Desisting and Delayed trajectories of SRVO were associated with violent and nonviolent official offending in adolescence and early adulthood, over and above the trajectories of SRNVO. In comparison, trajectories of SRNVO were weakly and inconsistently associated with official offending, once controlling for their overlap with trajectories of SRVO. CONCLUSIONS: Individuals on high trajectories of violent offending during adolescence are most at risk for being exposed to the justice system both concurrently and longitudinally. Differentiating violent and nonviolent offending can help resolve part of the discordance between self-reported and official offending.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".