Subtypes of adolescent offenders: affective traits and antisocial behavior patterns
Bibliographic record
Abstract
Etiological models of life-course persistent offending often emphasize behavioral explanations. Suggestions that persistent offenders have psychopathy ignore the distinct non-behavioral features of the psychopathy disorder. Using a three-factor model of the PCL-YV and cluster analysis with 259 incarcerated adolescents, we identified four distinct juvenile subtypes on the basis of affective, interpersonal, and behavioral dimensions. Prospective and retrospective comparisons of antisocial behavior patterns found the cluster comprising all three psychopathy dimensions to be the most chronic and severe. Impulsive features alone were strongly associated with severe antisocial behaviors retrospectively, but not prospectively. Findings rebut the proposal that disruptive behavioral and impulsive symptoms can identify "fledgling psychopaths." Assessments that disregard callous-unemotional traits will likely result in high false positive rates among serious adolescent offenders. Implications for developmental models of chronic offending are discussed in light of the need for further follow-up into adulthood.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".