The effect of discordance among violence and general recidivism risk estimates on predictive accuracy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it