Antisocial thinking in adolescents: Further psychometric development of the Antisocial Beliefs and Attitudes Scale (ABAS).
Bibliographic record
Abstract
Investigating the impact of "off-line" cognitive structures on the broad range of antisocial behaviors shown by young people has been hampered by the absence of psychometrically robust measures of antisocial cognitions. This study evaluates the psychometric properties of the Antisocial Beliefs and Attitudes Scale (ABAS), a developmentally sensitive measure of young people's beliefs and attitudes toward social standards of acceptable behavior at home and at school. The reliability and validity of the ABAS was assessed in a sample of British school children (N = 486) aged 9-16 years (M = 12.79, SD = 1.90) and male young offenders (N = 84) aged 13-17 years (M = 15.15, SD = 0.27). Participants completed the ABAS, together with a self-report measure of antisocial behavior; maternal reports of antisocial activity were also collected in the offending sample. Confirmatory factor analysis replicated the 2-factor structure of Rule Noncompliance and Peer Conflict previously derived from a sample of Canadian school children, and these factors showed good test-retest reliability. Rule Noncompliance predicted self-reported antisocial behavior for ages 11-16 years, while Peer Conflict predicted antisocial behavior for ages 9-16 years. Comparisons between young offenders and an age-matched subsample of males from the school group showed significant differences. In young offenders, Rule Noncompliance and Peer Conflict were significantly predictive of self-reported antisocial behavior, while Rule Noncompliance independently predicted mothers' ratings of their sons' antisocial behavior. These findings provide support for the ABAS as a psychometrically sound measure of antisocial thinking.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".