Primer on Risk Assessment and the Statistics Used to Evaluate Its Accuracy
Bibliographic record
Abstract
The pervasiveness of risk assessment in correctional decision-making necessitates a better understanding of the nature of risk scales and the methods used to assess their accuracy. Risk is a continuous dimension, which means that risk assessment is a prognostic task as opposed to a diagnostic task. Risk scales can also be considered criterion-referenced as opposed to norm-referenced. Predictive accuracy can be divided into discrimination and calibration. Area under the curves (AUCs), Cox regression, Harrell’s C , Cohen’s d , and logistic regression are appropriate for analyses of discrimination. There is no consensus on calibration statistics, but both chi-square tests and the Expected/Observed (E/O) index have been used and show promise. Statistics intended for dichotomous diagnostic decisions (e.g., positive predictive accuracy and negative predictive accuracy, number needed to detain, number needed to discharge) are inappropriate for risk scales because of the prognostic nature of risk scales. In many circumstances, diagnostic statistics provide more information about the base rate of recidivism than about the risk scale.
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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 | 0.030 | 0.130 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.015 |
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".