Field validity of Static-99R and STABLE-2007 with 4,433 men serving sentences for sexual offences in British Columbia: New findings and meta-analysis.
Bibliographic record
Abstract
Many forensic assessment measures are developed and validated under research conditions but applied in the field, where professionals or paraprofessionals have varied training, unknown fidelity to administration procedures, and contextual pressures related to their institutions or legal system. Yet few studies examine the generalizability of psychometric properties of these scales as actually applied in field settings. This study examined 4,433 individuals assessed by probation officers on the Static-99R or STABLE-2007 sexual recidivism risk scales in British Columbia, Canada. Sexual, violent, and any recidivism were examined. Static-99R and STABLE-2007 had moderate accuracy in discriminating recidivists from non-recidivists, and both scales added incrementally in predicting all three outcomes (with Static-99R demonstrating higher accuracy). Organizing the items into constructs, sexual criminality, general criminality, and youthful stranger aggression incrementally predicted all three outcomes. For violent and any recidivism, the incremental effect of sexual criminality was in the negative direction (i.e., high sexual criminality was associated with relatively lower rates of violent and any recidivism). Calibration analyses indicated that recidivism rates were lower than what would be predicted by the norms for the scales. The current study also presented a meta-analysis of 15 field validity studies of Static-99R and 4 field validity studies of STABLE-2007. Results of the current study and meta-analysis support the field application of Static-99R and STABLE-2007, while emphasizing the importance of training and proper implementation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.036 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.022 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".