OA20244 Risk assessment of sex offenders. Implications for advancing the prevention of sexual abuse
Bibliographic record
Abstract
Abstract Issue/Problem Sexual crimes, including child sexual abuse, rape, and child pornography, represent a significant societal problem. Description: The treatment of perpetrators is not always effective, and there is a persistent risk of recidivism. To address this, risk and mental health specialists employ a process known as risk assessment, which evaluates the likelihood of offenders reoffending or causing harm. While widely applied in countries such as the USA, England, and Canada, risk assessment is not currently implemented in Greece. Results Risk assessment involves identifying risk factors—characteristics of offenders or offenses that increase the probability of sexual or general criminal recidivism. These are categorized as static factors, which are stable, and dynamic factors, which can change, such as attitudes or interests. Protective factors, such as self-control and empathy toward victims, are also increasingly considered. Assessments are conducted primarily by psychologists, social workers, and other mental health specialists within judicial and correctional systems. The results inform decisions regarding offender management, including sentencing, prison treatment programs, parole, and post-release supervision, such as registration in sex offender registries. Several evidence-based tools are used internationally to guide evaluations, including SVR-20, STATIC-99, SORAG, RRASOR, VASOR-2, MnSOST-R, SOTIPS, Stable 2007, and Acute-2007. Lessons These tools, combined with expert judgment, allow for the classification of offenders by risk level (low, medium, high), facilitating targeted intervention strategies. Given that risk assessment is largely unknown in Greece, introducing validated tools and training professionals in their use could improve offender management. Key messages • Drawing on international experience while considering the Greek judicial and correctional context, implementing risk assessment could enhance prevention strategies and reduce the recurrence of sexual offenses. Topic sex offenders, risk assessment, recidivism, sexual crimes
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".