Attitudes, Associates, and Romantic Relationships: Validating the Measure of Criminal Attitudes and Associates (MCAA) in a Sample of Male and Female Adolescent Offenders
Bibliographic record
Abstract
Existing measures of criminal attitudes and criminal associates have rarely been tested with samples of female offenders nor have they considered the role of gender-informed variables such as criminal romantic relationships and relationship quality.Consequently, the study first examined the reliability and validity of the Measure of Criminal Attitudes and Associates (MCAA, originally designed for adult male offenders) on a sample of 312 adolescent offenders (102 females, 210 males).Second, the study examined whether or not the predictive validity of the associates component of the MCAA could be enhanced by directly incorporating two genderinformed variables: criminal romantic partner and relational quality (i.e., mutually empathic, authentic, and empowering relationships with criminal associates).The results indicate that the original MCAA is reliable for males (α = .65 to .91) and females (α = .62to .89).The results also illustrate that the Criminal Friend Index component of the MCAA predicts reconvictions for males (AUC = .64)but not females (AUC = .53),however the MCAA total attitude score predicts reconvictions for both genders (males, AUC = .61;females, AUC = .70).However, incorporating gender-informed variables into the associates domain of the MCAA did not improve predictive accuracy.The need to develop gender-informed measures of criminal attitudes and associates from the ground-up specifically for girls and women in conflict with the law is discussed.iii
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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 | 0.004 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".