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<em> low delinquency</em> trajectory groups, <em>p </em>&lt; .05 for both analyses. Furthermore, both the youth from the two <em>desisters</em> trajectory groups (13% of the sample) and from the two most at-risk trajectories (<em>escalators</em> and <em>high delinquency</em>, 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 <em>Criminal Justice</em> costs were due to the <em>high delinquency</em> and <em>escalators</em> trajectory groups. Antisocial or delinquent girls cost society more money than antisocial or delinquent boys in all domains, with the exception of the <em>Social Assistance</em> domain<strong>. </strong>Implications for crime prevention are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".