The Counterintuitive Consequences of Sex Offender Risk Assessments at Sentencing
Bibliographic record
Abstract
Virginia adopted a risk assessment to help determine sentencing for sex offenders. It was incorporated as a one-way ratchet toward higher sentences: expanding the upper end of the sentence guidelines by up to 300 per cent. This led to a sharp increase in sentences for those convicted of sexual assault. More surprisingly, it also led to a decrease in sentences for those convicted of rape. This raises two questions: (a) why did sentencing patterns change differently across these groups, and (b) why would risk assessment lead to a reduction in sentence length? The first question is relatively easy to answer. While both groups saw an expansion in the upper end of the sentencing guidelines, only sexual assault had the floor lifted on the lower end, making leniency more costly. The second question is less straightforward. One potential explanation is that the risk assessment served as a political or moral shield that implicitly justified leniency for those in the lowest risk category. Even though the risk assessment did not change sentencing recommendations for low-risk individuals, it provided a ‘second opinion’ that could mitigate blame or guilt should the low-risk offender go on to reoffend. This decreased the risks of leniency and counterbalanced any increase in severity for high-risk individuals.
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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 | 0.011 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".