A Comparison of Predictors of General and Violent Recidivism Among High-Risk Federal Offenders
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
The accuracy of 10 risk measures in predicting general and violent recidivism among 106 federally sentenced male offenders was compared. During an average period of opportunity to reoffend of 713 days ( SD = 601.38), 28 offenders recidivated nonviolently, and 34 recidivated violently. Common language effect sizes in discriminating violent recidivists from other offenders were .73 for the General Statistical Information on Recidivism–Revised and .72 for the Violence Risk Appraisal Guide. Effect sizes ranging from .58 to .68 were obtained for DSM-IV Conduct Disorder scored as a scale, the Violent Statistical Information on Recidivism–Revised, the Psychological Referral Screening Form, the Psychopathy Checklist–Revised total score and Factor 2, and the Childhood and Adolescent Taxon Scale. Effect sizes of .58 and .51 were obtained with the DSM-IV Antisocial Personality Disorder scored as a scale and the Psychopathy Checklist Factor 1, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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