The Predictive Validity of Sex Offender Treatment Progress Scale (SOTIPS) Using Iowa Sex Offenders
Bibliographic record
Abstract
This study is a replication study of McGrath, Lasher, and Cumming's 2012 study. McGrath et al.'s population consisted of convicted Vermont sex offenders, while this replication study's population consisted of convicted Iowa sex offenders. Nearly 2,000 sex offenders are under the Iowa Department of Corrections community supervision (Daily Statistics-DOC Offender, 2023). Iowa uses SOTIPS, Static-99R, and the Iowa Risk Revised (IRR) to determine recidivism risk and supervision levels. The Static-99R was developed in Canada and has been validated in several jurisdictions, including Iowa (Statistical validation of the ISORA8 & Static-99 final report, 2010). The IRR was developed in Iowa using 6,337 Iowa offenders and validated using a sample of 9,387 Iowa offenders (Fischer, 1980). No other study has examined the predictive validity of SOTIPS using Iowa sex offenders. The observational data will be analyzed using predictive statistics such as regression techniques. This replication study will follow McGrath et al.'s (2012) study of data analysis. This researcher used Area Under the Curve (AUC) to measure the accuracy of the Static-99R and SOTIPS. The AUC values and confidence levels were calculated using the trapezoidal method in SPSS. Cox regression survival analysis compared the predictive accuracy of Static-99R, the static risk factors of SOTIPS, and the dynamic risk factors scores.
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How this classification was reachedexpand
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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".