The Relative Utility of Fixed and Variable Risk Factors in Discriminating Sexual Recidivists and Nonrecidivists
Bibliographic record
Abstract
This study compared the relative utility of fixed and variable risk factors in discriminating between recidivist and nonrecidivist sexual offenders. Subjects were 95 adult male offenders released from the Canadian federal correctional system between 1988 and 1992. Risk factors from the Sexual Violence Risk--20 (SVR-20; D. P. Boer, S. D. Hart, P. R. Kropp, & C. D. Webster, 1997) were coded from prerelease institutional records; sexual and nonsexual violent recidivism was coded from postrelease police and correctional records. SVR-20 risk factors were categorized as fixed (static) or variable (dynamic) markers according to the criteria of H. C. Kraemer et al. (1997); the fixed risk markers were further divided into offense history and psychosocial factors. Hierarchical Cox regression survival analyses were conducted to compare the relative contribution of fixed offense history, fixed psychosocial, and variable psychosocial risk markers in accounting for any violent recidivism and sexually violent recidivism. Analyses indicated that fixed psychosocial factors added little to the models comprised fixed offense history factors alone. There was some evidence that variable psychosocial factors had incremental validity when added to predictions made on the basis of fixed factors, particularly in the prediction of sexual violence. The individual factors that were included in the final models are consistent with previous findings, and support the use of sexual deviance and antisocial lifestyle variables in the prediction of recidivism among sexual offenders.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".