Improving forensic tribunal decisions: the role of the clinician
Bibliographic record
Abstract
Three empirical investigations of forensic decision-making were conducted: a study of 104 hearings by a forensic tribunal; an evaluation of which aspects of forensic patients' clinical presentation were empirical predictors of violence; and a survey of forensic clinicians to determine which factors they said they used to assess risk of violent recidivism and which they actually used. Results showed a significant correlation between actuarial risk and clinical advice to the tribunal, and a nonsignificant trend for patients higher in actuarial risk to receive more restrictive dispositions. Psychotic diagnoses and symptoms were not indicators of increased risk of violent recidivism. Clinicians endorsed some empirically valid indicators of risk, but also relied on some invalid indicators. There was also inconsistency between factors clinicians said they used and factors actually related to their hypothetical decision-making. An automated system is presented as an illustration of how the consistency and validity of forensic decisions could be enhanced.
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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 | 0.060 | 0.291 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".