Predicting and understanding risk of re-offending: the Prisoner Cohort Study
Bibliographic record
Abstract
Risk assessment and risk management are key components of the Government’sproposals to detain and treat individuals with Dangerous and Severe PersonalityDisorder (DSPD). Accuracy in risk assessment plays a major role in identifi cation ofthe small group of individuals thought to pose a very high risk of harm to society andin monitoring their level of risk during and after treatment (Douglas et al., 2005). ThePrisoner Cohort Study was a research project originally commissioned by the HomeOffi ce as part of the DSPD programme to evaluate the predictive accuracy of a rangeof currently available risk assessment instruments for future violent and sexual reoffending.The main aims of the study were to test the accuracy in a UK populationof the risk assessment devices and instruments currently being piloted for use in theDSPD centres in predicting serious re-offending, and to identify the best instruments interms of their accuracy in prediction. Most risk assessment instruments included in thisstudy were previously validated on US/Canadian male prisoners or forensic patientswithout further differentiation. The study also examined the prevalence of offenderspotentially classifi able as having DSPD on the basis of the currently available riskinstruments and personality assessments, and their dangerousness in terms of reoffendingafter release into the community. This report focuses on male offenders andviolent re-offending. Data collection for sexual re-offending and for female offenderswas ongoing at the time of the preparation of this report. Findings are thereforepresented for men serving determinate sentences for violent or sexual index offencesinterviewed in the fi rst phase of the study (N=1396).
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".