Primer on Risk Assessment and the Statistics Used to Evaluate Its 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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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