Latent Constructs of the 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 (e.g., characteristics of prior offences). Consequently, they are assumed to be unable to directly inform the selection of treatment targets or evaluate change. In this article, we argue that this limitation can be mitigated by using latent variable models as a framework to link historical risk factors to the psychological characteristics of offenders. Accordingly, we conducted a factor analysis of the 13 nonredundant items from the two most commonly used risk tools for sexual offenders (Static-99R and Static-2002R) to identify the psychological information contained in these tools. Three factors were identified: (a) persistence/paraphilia, a construct related to sexual criminality, especially of the pedophilic type; (b) youthful stranger aggression, a construct centered on young age and offence seriousness; and (c) general criminality, a construct that reflected the diversity and magnitude of criminal careers. These constructs predicted sexual recidivism with similar accuracy, but only youthful stranger aggression and general criminality predicted nonsexual recidivism. These results indicate that risk tools for sexual violence are multidimensional, and support a shift from a focus on atheoretical risk markers to the assessment of 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 | 0.006 | 0.020 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".