Evading Detection during Adolescence: The Role of Criminal Capital and Psychosocial Factors
Bibliographic record
Abstract
Many adolescents engage in crime, but not all youth are caught by law enforcement. Previous work highlights the importance of criminal capital, or assets that help individuals evade police detection. Few studies have extended this work to adolescent offender populations or have considered the contribution of psychosocial and contextual factors to arrest avoidance. The current study uses data from a longitudinal study of first-time adolescent offenders to evaluate the contribution of criminal capital, psychosocial and contextual variables in predicting re-arrest. The results from the longitudinal random effect logit models confirm the contribution of established criminal capital variables in predicting arrest but also highlight the role of psychosocial predictors (future expectations and intelligence). Contextual factors such as parenting and neighborhood disorder had no association with the likelihood of re-arrest. These findings highlight several factors that help youth avoid re-arrest, and may exacerbate continued patterns of illegal behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".