Are risk assessment tools more accurate than unstructured judgments in predicting violent, any, and sexual offending? A meta‐analysis of direct comparison studies
Bibliographic record
Abstract
We conducted a pre-registered meta-analysis of studies that directly compared the predictive validity of risk assessment tools to unstructured judgments of risk for violent, any, or sexual offending. A total of 31 studies, containing 169 effect sizes from 45,673 risk judgments, met inclusion criteria. Based on the results of three-level mixed-effects meta-regression models, the predictive validity of total scores on risk assessment tools was significantly higher than that of unstructured judgments for predictions of violent, any, and sexual offending. Tools continued to outperform unstructured judgments after accounting for risk of bias. This finding was also robust to variations in population, assessment context, and outcome measurement. Although this meta-analysis provides support for the use of risk assessment tools, it also highlights limitations and gaps that future research should address.
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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 | 0.037 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".