Transcending the Actuarial Versus Clinical Polemic in Assessing Risk for Violence
Bibliographic record
Abstract
Much energy has been expended over recent years in debating the relative merits of actuarial versus clinical approaches to violence risk prediction. Although it has gradually become apparent that scores based on more or less static factors obtainable from the record do indeed associate with outcome violence over years of follow-up, there is no reason to suppose that, at least potentially, dynamic variables do not hold as much or more promise when it comes to projections over weeks or months. Clinicians involved in release decision-making might wish to consider the following, in order of importance: (a) the legal framework within which the decision is being made, (b) the thoroughness with which scientific methods have been applied to the particular case at issue, (c) the precision of the individualized statement of violence risk being offered, (d) the steps which could be taken to reduce that risk, and (e) if available, the individual's violence risk assessment score in relation to already amassed pertinent statistical data.
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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 | 0.151 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.003 |
| Science and technology studies | 0.002 | 0.049 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".