General Criminal Dynamic Risk and Strength Factors Predict Short-Term General Recidivism Outcomes Among People Convicted of Sexual Crime During Community Supervision
Bibliographic record
Abstract
There are clinical practice and operational reasons why it may be appropriate to primarily focus on general risk factors when supervising people convicted of sexual crime in the community. General risk domains may be particularly relevant when supervision officers engage in frequent reassessment of acute dynamic risk factors. We tested the ability of a case management tool, the Dynamic Risk Assessment for Offender Re-entry, to discriminate community based, short-term general (all outcome) recidivism versus nonrecidivism among people convicted of sexual crime ( n = 562). We tested the predictive discrimination validity of each DRAOR item and then subscale scores in univariate and multivariate models (also controlling for general static risk). DRAOR scores were associated with general recidivism outcomes and effect sizes were generally similar or stronger compared to models with people convicted of nonsexual crime ( n = 2854). DRAOR Acute scores were consistently and incrementally related to general recidivism outcomes beyond other scores. In practice, case managers should remain aware that people convicted of sexual crime are at risk for nonsexual recidivism outcomes and assess problematic functioning broadly alongside problems in sexual domains. Clinically, interconnection among domains potentially provides multiple avenues for effective intervention.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".