Absolute Recidivism Rates Predicted By Static-99R and Static-2002R Sex Offender Risk Assessment Tools Vary Across Samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There has been considerable research on relative predictive accuracy (i.e., discrimination) in offender risk assessment (e.g., Are high-risk offenders more likely to reoffend than low-risk offenders?), but virtually no research on the accuracy or stability of absolute recidivism estimates (i.e., calibration). The current study aimed to fill this gap by examining absolute and relative risk estimates for certain Static sex offender assessment tools. Logistic regression coefficients for Static-99R and Static-2002R were combined through meta-analysis (8,106 sex offenders; 23 samples). The sexual recidivism rates for typical sex offenders are lower than the public generally believes. Static-99R and Static-2002R both demonstrated remarkably consistent relative predictive accuracy across studies. For both scales, however, the predicted recidivism rates within each risk score demonstrated large and significant variability across studies. The authors discuss how the variability in recidivism rates complicates the estimation of recidivism probability in applied assessments.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it