Intelligence, Executive Functions, and Decision Making as Predictors of Antisocial Behavior in an Adolescent Sample of Justice‐Involved Youth and a Community Comparison Group
Bibliographic record
Abstract
Abstract A clinical sample of justice‐involved male adolescents and a community comparison group were compared on a battery of cognitive ability tasks (intelligence and executive functions), decision making measures, and other individual difference measures, including ratings of self‐control, recognition of morally debatable behaviors, and antisocial beliefs. The clinical sample displayed lower performance on cognitive abilities and decision making than the community comparison group. In particular, the clinical group displayed less otherside thinking and more hostile attribution biases in unintentional situations compared with the community comparison group. Cognitive abilities and the decision making performance predicted group membership. Then, group membership, ratings of self‐control, attitudes about morally debatable behaviors, and antisocial beliefs predicted ratings of antisocial behavior in the full sample. These findings suggest that measures of cognitive ability and decision making make separate contributions to explaining antisocial behaviors. In addition, the predictors of group membership and antisocial behavior did not overlap, suggesting that antisocial behavior engagement in clinical samples may be separable from the continuum of antisocial behavior across the full sample. Cognitive science models of decision making can provide a framework for understanding antisocial behavior in clinical and community samples of adolescents. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 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".