Kriminalprognose und Sexualdelinquenz - Möglichkeiten und Grenzen standardisierter Kriminalprognosemethoden bei Sexualstraftätern
Bibliographic record
Abstract
The present research project addresses standardized recidivism risk prediction methods for released sexual offenders. Originally developed in the Anglo-American language area, standardized risk instruments are meanwhile in the German-speaking part of Europe regarded as state of the art as well. Using a prospective longitudinal research design we examined the inter-rater reliability and the concurrent and predictive validity of the German versions or adaptations of the most commonly used standardized risk assessment instruments for sexual offenders, the Rapid Risk Assessment for Sexual Offense Recidivism (RRASOR), Static-99, the Sexual Offender Risk Appraisal Guide (SORAG), the Sexual Violence Risk-20 (SVR-20), and the Psychopathy Checklist-Revised (PCL-R). On the one hand, the results of the present studies indicate satisfactory reliability and validity and therefore support the predictive value of these risk tools. On the other hand, particularly the predictive validity varied depending on instrument, offender subgroup and recidivism category. Consequently, practitioners have to take these limitations into consideration when they use standardized recidivism risk prediction tools for sexual offenders.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".