The effect of discordance among violence and general recidivism risk estimates on predictive accuracy
Bibliographic record
Abstract
INTRODUCTION: Previous research has shown that the prediction of short-term inpatient violence is negatively affected when clinicians' inter-rater agreement is low and when confidence in the estimate of risk is low. This study examined the effect of discordance between risk assessment instruments used to predict long-term general and violence risk in offenders. METHODS: The Psychopathy Checklist - Revised (PCL-R), Level of Service Inventory - Revised (LSI-R), Violence Risk Appraisal Guide (VRAG), and the General Statistical Information on Recidivism (GSIR) were the four risk-prediction instruments used to predict post-release general and violent recidivism within a sample of 209 offenders. RESULTS: The findings lend empirical support to the assumption that predictive accuracy is threatened where there is discordance between risk estimates. Discordance between instruments had the impact of reducing predictive accuracy for all instruments except the GSIR. Further, the influence of discordance was shown to be greater on certain instruments over others. Discordance had a moderating effect on both the PCL-R and LSI-R but not on the VRAG and GSIR. CONCLUSIONS: There is a distinct advantage when attempting to predict recidivism to employing measures such as the LSI-R, which includes dynamic variables and intervention-related criminogenic domains, over a measure purely of fixed characteristics, such as the GSIR; however, if there is discordance between the risk estimates, caution should be exercised and more reliance on the more static historically based instrument may be indicated.
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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 | 0.122 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".