Callous-unemotional traits and adolescents’ role in group crime.
Bibliographic record
Abstract
The current study examined the association of callous-unemotional (CU) traits with group offending (i.e., committing a crime with others; gang involvement) and with the role that the offender may play in a group offense (e.g., being the leader). This analysis was conducted in an ethnically and racially diverse sample (N = 1,216) of justice-involved adolescents (ages 13 to 17) from 3 different sites. CU traits were associated with a greater likelihood of the adolescent offending in groups and being in a gang. Importantly, both associations remained significant after controlling for the adolescent's age, level of intelligence, race and ethnicity, and level of impulse control. The association of CU traits with gang membership also remained significant after controlling for the adolescent's history of delinquent behavior. Further, CU traits were associated with several measures of taking a leadership role in group crimes. CU traits were also associated with greater levels of planning in the group offense for which the adolescent was arrested, although this was moderated by the adolescent's race and was not found in Black youth. These results highlight the importance of CU traits for understanding the group process involved in delinquent acts committed by adolescents. They also underscore the importance of enhancing the effectiveness of treatments for these traits in order to reduce juvenile delinquency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".