Assessing Risk in North Dakota Juvenile Probation: A Preliminary Examination of the Predictive Validity of the Youth Assessment and Screening Instrument
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This exploratory research examines the predictive accuracy of the Youth Assessment and Screening Instrument adopted by the North Dakota Juvenile Court through a retrospective review of assessment and court records. While studies of YASI from New York, Virginia, and Canada provide some confidence in the instrument’s predictive validity, questions remain concerning its accuracy among female and other specialized populations. This study finds a moderate effect for the instrument’s predictive accuracy in relation to general reoffending from a random sample of juvenile probationers (AUC = 0.66, p = .002, 95% CI [0.56, 0.75], N = 139), but results were notably weaker for females compared to males. Further research is needed on its accuracy among African American and Native American youth.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it