The accuracy of recidivism risk assessments for sexual offenders: A meta-analysis of 118 prediction studies.
Bibliographic record
Abstract
This review compared the accuracy of various approaches to the prediction of recidivism among sexual offenders. On the basis of a meta-analysis of 536 findings drawn from 118 distinct samples (45,398 sexual offenders, 16 countries), empirically derived actuarial measures were more accurate than unstructured professional judgment for all outcomes (sexual, violent, or any recidivism). The accuracy of structured professional judgment was intermediate between the accuracy found for the actuarial measures and for unstructured professional judgment. The effect sizes for the actuarial measures were moderate to large by conventional standards (average d values of 0.67-0.97); however, the utility of the actuarial measures will vary according to the referral question and samples assessed. Further research should identify the psychologically meaningfully factors that contribute to risk for reoffending. (PsycINFO Database Record (c) 2009 APA, all rights reserved).
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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 | 0.038 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".