Does Consideration of Psychopathy and Sexual Deviance Add to the Predictive Validity of the Static-99R?
Bibliographic record
Abstract
The Static-99 is the most commonly used actuarial risk assessment tool for the prediction of sexual recidivism. In addition, the use of psychopathy and sexual deviance has been common in assessing sexual offenders, based on research suggesting that these factors have predictive validity. It has also become common practice to modify risk assessments based on the Static-99/99R because of the presence of psychopathy and indicators of deviant sexual interests, although to date there has been no research validating this procedure. The current research was conducted to fill this gap in the literature. Using a sample of 272 sexual offenders, the extent to which psychopathy, sexual deviance, and their interaction added to the predictive validity of the Static-99R was examined. Analyses were conducted using the whole sample as well as subgroups of rapists and child molesters. It was found that although the Static-99R predicted sexual recidivism, adding psychopathy and sexual deviance in a Cox regression analysis did not improve the prediction. This held true for child molesters when examined on their own. For rapists, although psychopathy and sexual deviance did not contribute to the prediction of sexual recidivism, for serious (i.e., violent including sexual) recidivism, the inclusion of psychopathy added to the prediction. Results are discussed in terms of implications for practice.
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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 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".