Predictive validity of Stable-2007 in incarcerated samples
Bibliographic record
Abstract
Some are unclear whether risk assessment instruments, specifically dynamic risk instruments, have demonstrated utility in the risk estimation, treatment recommendations, and monitoring change over time in men at risk for or under sentence of Indeterminate Detention (ID) for sexual offenses. We compare two datasets, the first consisting of individuals representing a routine sample of persons convicted of a sexual offense and the second of men representative of a high risk/needs sample. These two distinct samples (n = 442, mean Static-99R score = 2.4; n = 168, mean Static-99R score 4.5) were then also scored on the Stable-2007. For both groups this scoring occurred in an institutional setting. The Stable-2007 predicted sexual recidivism in Sample 1 independently and in conjunction with the Static-99R. In the high-risk sample the results were the same. In both samples a compound outcome variable (Sexual + Violent reoffense) was also calculated with the Stable-2007 predicting the compound outcome variable in Sample 1 but not Sample 2. This is interesting in that it suggests that the Stable-2007 assesses constructs specific to sexual re-offense in higher risk offenders and not general traits of violence or common anti-social behaviour. Limitations and directions for further research are discussed.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".