Evidence for Risk Estimate Precision: Implications for Individual Risk Communication
Bibliographic record
Abstract
Actuarial risk assessment instruments using well-established predictor variables measured at the individual level (e.g., age, criminal history, psychopathy) discriminate well between recidivists and non-recidivists across diverse samples. Data indicating the relative risk of recidivism can inform policy decisions about allocating resources according to risk within a correctional system, consistent with the first of the risk-need-responsivity (RNR) principles. Evidence for the precision of absolute risk as applied to an individual based on scores from many samples, however, has proven challenging. In this paper, we present a study examining the association of actuarial risk estimate precision with sample size using the Post Conviction Risk Assessment (PCRA; Lowenkamp et al., 2013), in samples of up to 26,642 offenders. Results indicate that the precision of individual estimates can be demonstrated with sufficient sample size. We believe that the implications of absolute risk for the communication of an individual offender's risks have been poorly understood. We argue that the purpose of individual-level risk communication is to ensure the effective application of policy, which requires matching a new case to aggregate data. We illustrate how an offender's risk might thus be communicated, and conclude that this function is distinct from management of an individual's criminogenic needs and identification of effective and suitable treatments.
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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 | 0.323 | 0.788 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".