Prediction of Recidivism in Exhibitionists: Psychological, Phallometric, and Offense Factors
Bibliographic record
Abstract
Exhibitionists have traditionally been regarded as nuisance offenders. However, empirical studies show that some offenders can be highly recidivistic and can escalate to incidents of Hands-on sexual assault. The objective of this study was to investigate predictors of recidivism in exhibitionists and clarify the differences between Hands-on and Hands-off sexual recidivists. The hundred and twenty-one exhibitionists were assessed at a university teaching hospital between 1983 and 1996. Archival data came from medical files and police files. The Psychopathy Checklist-Revised (PCL-R) was assessed retrospectively. Results indicated that over a mean follow-up period of 6.84 years, 11.7, 16.8, and 32.7% of exhibitionists were charged with or convicted of sexual, violent, or criminal offenses, respectively. Sexual reoffending recidivists were less educated, and had more prior sexual and criminal offenses. Violent, recidivists were also less educated, had lower Derogatis Sexual Functioning Inventory (DSFI) scores, higher PCL-R Totals, and more prior sexual, violent, and criminal offenses. Criminal recidivists were younger, less educated, had lower DSFI scores, higher PCL-R scores, higher Pedophile Indices, and more prior sexual, violent, and criminal offenses. Hands-on sexual recidivists demonstrated higher PCL-R ratings, higher Pedophile and Rape indices, and more prior sexual, violent, and criminal offenses than did Hands-off counterparts.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".