Should Actuarial Risk Assessments be Used with Sex Offenders who are Intellectually Disabled?
Bibliographic record
Abstract
Background Objective actuarial assessments are critical for making risk decisions, determining the necessary level of supervision and intensity of treatment ( Andrews & Bonta 2003 ). This paper reviews the history of organized risk assessment and discusses some issues in current attitudes towards sexual offenders with intellectual disabilities. Method We present two risk assessment tools (RRASOR and STABLE‐2000) that appear to have practical utility with this population. Data are presented from a community sample of 81 sexual offenders who are intellectually disabled suggesting that the RRASOR may provide a useful metric of risk for this population. Dynamic risk is assessed using the STABLE‐2000. This tool, based on 16 areas empirically associated with sexual recidivism, samples the individuals’ current behaviour, skill deficits and personality factors. Change in these factors serves to flag the supervisor to changing risk levels. Conclusions In addressing the question of whether we should seek special risk measures normed on people with intellectually disabilities, given the current lack of alternative tools, we conclude that it is reasonable to make use of the risk assessments that have been validated on the general sex offender population.
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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 | 0.022 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".