The Relationship Between Static and Dynamic Risk Factors and Reconviction in a Sample of U.K. Child Abusers
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 study examined how well historical information and psychometric data predicted sexual recidivism in a sample of child abusers about to undergo group-based cognitive behavioral treatment in the community. Static, historical factors, as measured by the Static-99 (R. K. Hanson & D. Thornton, 2000), significantly predicted recidivism over the 6-year follow-up period. High-risk men were over 5 times more likely to be reconvicted for a sexual offence compared to low-risk men. Adding psychometric measures of dynamic risk (e.g., pro-offending attitudes, socio-affective problems) significantly increased the accuracy of risk prediction beyond the level achieved by the actuarial assessment of static factors. This result indicates the importance of considering dynamic risk factors in any comprehensive risk protocol.
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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) | 0.000 | 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 | 0.000 |
| 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