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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".