Progress in Violence Risk Assessment and Communication: Hypothesis versus Evidence
Bibliographic record
Abstract
We draw a distinction between hypothesis and evidence with respect to the assessment and communication of the risk of violent recidivism. We suggest that some authorities in the field have proposed quite valid and reasonable hypotheses with respect to several issues. Among these are the following: that accuracy will be improved by the adjustment or moderation of numerical scores based on clinical opinions about rare risk factors or other considerations pertaining to the applicability to the case at hand; that there is something fundamentally distinct about protective factors so that they are not merely the obverse of risk factors, such that optimal accuracy cannot be achieved without consideration of such protective factors; and that assessment of dynamic factors is required for optimal accuracy and furthermore interventions aimed at such dynamic factors can be expected to cause reductions in violence risk. We suggest here that, while these are generally reasonable hypotheses, they have been inappropriately presented to practitioners as empirically supported facts, and that practitioners' assessment and communication about violence risk run beyond that supported by the available evidence as a result. We further suggest that this represents harm, especially in impeding scientific progress. Nothing here justifies stasis or simply surrendering to authoritarian custody with somatic treatment. Theoretically motivated and clearly articulated assessment and intervention should be provided for offenders, but in a manner that moves the field more firmly from hypotheses to evidence.
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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 | 0.314 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.039 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".