Three Central Dimensions of Sexual Recidivism Risk: Understanding the Latent Constructs of Static-99R and Static-2002R
Bibliographic record
Abstract
The most commonly used risk assessment tools for predicting sexual violence focus almost exclusively on static, historical factors. Consequently, they are assumed to be unable to directly inform the selection of treatment targets, or evaluate change. However, researchers using latent variable models have identified three dimensions in static actuarial scales for sexual offenders: Sexual Criminality, General Criminality, and a third dimension centered on young age and aggression to strangers. In the current study, we examined the convergent and predictive validity of these dimensions, using psychological features of the offender (e.g., antisocial traits, hypersexuality) and recidivism outcomes. Results indicated that (a) Sexual Criminality was related to dysregulation of sexuality toward atypical objects, without intent to harm; (b) General Criminality was related to antisocial traits; and (c) Youthful Stranger Aggression was related to a clear intent to harm the victim. All three dimensions predicted sexual recidivism, although only General Criminality and Youthful Stranger Aggression predicted nonsexual recidivism. These results indicate that risk tools for sexual violence are multidimensional, and support a shift from an exclusive focus on total scores to consideration of subscales measuring psychologically meaningful constructs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".