Violent Sex Offenses: How are they Best Measured from Official Records?
Bibliographic record
Abstract
In the United States, sexually violent predator (SVP) commitment statutes generally require assessment of an offender's risk of subsequent sexual violence. Current actuarial methods for predicting sexual reoffending were actually designed to predict something else-charges or convictions for offenses deemed sexual based on information obtained from police "rapsheets" alone. This study examined the referral and past offenses of 177 sex offenders. Results showed that police rapsheets (and data based on them) underestimated the number and severity of sexually motivated violent offenses for which sex offenders were actually apprehended. Rapsheet violent offenses seemed a more accurate index of the conduct addressed by SVP legislation than were rapsheet sex offenses. We suggest that, when evaluating sex offenders for SVP status, actuarial instruments designed to predict violent recidivism (as measured by rapsheet violent reoffenses) might be preferable to those designed to predict sexual recidivism (as measured by rapsheet sexual reoffenses).
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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 | 0.046 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".