Bibliographic record
Abstract
The appraisal of risk among sex offenders has seen recent advances through the advent of actuarial assessments. Statistics derived from Relative Operating Characteristics (ROCs) permit the comparison of predictive accuracies achieved by different instruments even among samples that exhibit different base rates of recidivism. Such statistics cannot, however, solve problems introduced when items from actuarial tools are omitted, when reliability is low, or when there is high between-subject variability in the duration of the follow-up. We present empirical evidence suggesting that when comprehensive actuarial tools (VRAG and SORAG) are scored with high reliability, without missing items, and when samples of offenders have fixed and equal opportunity for recidivism, predictive accuracies are maximized near ROC areas of 0.90. Although the term "dynamic" has not been consistently defined, such accuracies leave little room for further improvement in long-term prediction by dynamic risk factors. We address the mistaken idea that long-term, static risk levels have little relevance for clinical intervention with sex offenders. We conclude that highly accurate prediction of violent criminal recidivism can be achieved by means of highly reliable and thorough scoring of comprehensive multi-item actuarial tools using historical items (at least until potent therapies are identified). The role of current moods, attitudes, insights, and physiological states in causing contemporaneous behavior notwithstanding, accurate prediction about which sex offenders will commit at least one subsequent violent offense can be accomplished using complete information about past conduct.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".