Evading Detection during Adolescence: The Role of Criminal Capital and Psychosocial Factors
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.
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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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 it