Competence to Waive Interrogation Rights and Adjudicative Competence in Adolescent Defendants: Cognitive Development, Attorney Contact, and Psychological Symptoms.
Bibliographic record
Abstract
Although there is growing evidence of developmental differences in competency to waive interrogation rights and adjudicative competence, the correlates of adolescents' legal capacities remain unclear. This study examined the relationship of legal capacities to cognitive development, legal learning opportunities, and psychological symptoms. Participants were 152 male and female defendants aged 11-17, who completed Grisso's Instruments for Assessing Understanding and Appreciation of Miranda Rights, the Fitness Interview Test (Revised Edition), the Woodcock-Johnson III Cognitive Assessment Battery, and the Brief Psychiatric Rating Scale for Children. Legal capacities relevant to interrogation and adjudication increased with age. These developmental differences were partially mediated or explained by cognitive development. Of the specific cognitive abilities examined (general intellectual ability, verbal ability, reasoning, long-term retrieval, attention, and executive functioning), verbal ability was a particularly strong predictor of performance on competency measures. Also, defendants obtained lower scores on competency measures if they showed evidence of attention deficits or hyperactivity, had spent limited time with their attorneys, and/or were from low socioeconomic backgrounds.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".