How accurately can researchers measure criminal history, sexual deviance, and risk of sexual recidivism from self-report information alone?
Bibliographic record
Abstract
Sexual recidivism risk measures are primarily scored using official documentation (e.g. criminal records), but such reviews are time-consuming, and limited by the quality and availability of relevant information. In this study, we examined the agreement between self-reported and official file information. We conducted secondary analyses on two datasets in which 24 and 27 adult males convicted of sexual offences provided self-report information under confidential conditions, which we used to score the Static-99 and the Screening Scale for Pedophilic Interests. Criminal history information was reliable across both studies, whereas victim characteristics were not. We also used self-reports to create a self-report risk scale – the Sexual Offence Self-Report Risk Scale, which was positively correlated with the Static-99 across both studies (r = .73 and .56). Our results suggest that some self-report information gathered under confidential conditions can be reliable and provide acceptably valid estimates of relative risk for research purposes when official documentation is limited.
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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 | 0.110 | 0.345 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".