GOVERNMENT COSTS ASSOCIATED WITH DELINQUENT TRAJECTORIES
Bibliographic record
Abstract
The objectives of this project were to: (a) identify early trajectories of delinquency for both boys and girls at ages 8 (Grade 3), 11 (Grade 6), and 14 (Grade 9) in a longitudinal sample of 842 at-risk youth from a multi-informant perspective (i.e., parents, teachers, self-reported youth ratings), and (b) estimate the costs associated with each delinquency trajectory on utilization of resources in the criminal justice system, remedial education, health care and social services, and social assistance. The results indicated six distinct trajectories of delinquency: two low groups, two desisting groups, an escalator group, and a high delinquency group. There were significantly more females than males in the two low delinquency trajectory groups, p < .05 for both analyses. Furthermore, both the youth from the two desisters trajectory groups (13% of the sample) and from the two most at-risk trajectories (escalators and high delinquency, 5% of the sample) each accounted for approximately 40% of the estimated costs to government. It is interesting to note that 80% of the estimated Criminal Justice costs were due to the high delinquency and escalators trajectory groups. Antisocial or delinquent girls cost society more money than antisocial or delinquent boys in all domains, with the exception of the Social Assistance domain. Implications for crime prevention are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".