Is More Better? Combining Actuarial Risk Scales to Predict Recidivism Among Adult Sex Offenders.
Why this work is in the frame
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Bibliographic record
Abstract
The present study was conducted to determine whether combining the results of multiple actuarial risk scales increases accuracy in predicting sex offender recidivism. Multiple methods of combining 4 validated actuarial risk scales--the Violence Risk Appraisal Guide, the Sex Offender Risk Appraisal Guide, the Rapid Risk Assessment for Sexual Offense Recidivism, and the Static-99--were evaluated in a sample of 215 adult male sex offenders. These included the intuitively appealing believe-the-negative and believe-the-positive rules, adapted from medical decision making; the combination of absolute decision thresholds across a range of cutoff scores; and the statistical optimization methods of logistic regression and principal components analyses. No combination method provided a statistically significant or consistent advantage over the predictive accuracy of the single best actuarial scale.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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