Risky Business: The Role of Psychopathy and Violence Risk Assessment in Forensic Decision-Making
Bibliographic record
Abstract
Forensic decision-making is often subject to irrelevant influences (Hilton & Simmons, 2001) and disregards pertinent risk factors (McKee et al., 2007).Although this seems to be improving (Crocker et al., 2014), research has failed to examine if any progress has been made in almost a decade.Forensic psychiatric hospital files were retrospectively coded for 89 male Not Criminally Responsible on Account of Mental Disorder patients that had a Review Board (RB) hearing between 2007-2014 to investigate whether items from four empirically supported risk measures, the Violence Risk Appraisal Guide, the Historical Clinical Risk Management-20, the Psychopathy Checklist-Revised, and the Structured Assessment of Protective factors were considered.Just over half of expert reports and one quarter of RB rationales noted use of a structured risk assessment which increased over time.Despite inconsistency of use, empirically supported factors were frequently discussed and centered on mental health, treatment, criminal history, and reintegration.Overall, disposition decisions were predicted by discussion of both empirically supported risk and protective factors, however, still appear to be biased by irrelevant influences such as attractiveness.In spite of improvement, the results highlight the need for policies to ensure greater structure in how risk assessments are implemented into the decisionmaking process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".