Assessment Measures For Sexual Predators: Step-by-Step Guidelines
Bibliographic record
Abstract
Abstract As a result of recent legislative changes in both the United States and Canada regarding sexually violent predators (Glancy, Regehr, 2001; Zonona, 1999), mental health practitioners are increasingly being called upon to provide predictions regarding the future dangerousness of convicted sex offenders. Prediction of dangerousness based solely on clinical assessments for offenders of any kind and in particular sexual offenders, however, has proved to be remarkably inaccurate and to result in very low interrater reliability between professional assessors (Hilton, Simmons, 2001). Consequently, there has been considerable effort in the past decade to develop actuarial tools with the aim of improving predictive accuracy. Developers of the tools have reported favorable results in terms of predictive validity, but, nevertheless, considerable controversy exists about the place of actuarial testing in the assessment of sexual offenders (Zonona, 2000; Sreenivasan, Kirkish, Garrick, Wineberger, Phenixa, 2000). The original actuarial instruments focused exclusively on “static” or historical factors such as the age at first offence and the nature of violent offenses. The developers suggested that these tools for the prediction of dangerousness are accurate enough to be used in isolation and that adjunctive clinical assessments not only may fail to add to the predictive validity but in fact may be detrimental (Quinsey, Khanna,, Malcolm, 1998).
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.015 |
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".