Introduction to Special Issue “Statistical Issues and Innovations in Predicting Recidivism”
Bibliographic record
Abstract
Risk assessment is one of the most common tasks in the criminal justice system, yet most professionals in this field receive little to no formal training in statistical techniques for predicting dichotomous outcomes, such as recidivism. The purpose of this special issue was to help fill this gap in training and resources. We wanted to make some of the latest statistical issues and advances in predicting recidivism accessible to the readership of Criminal Justice and Behavior. In this introductory paper, we briefly describe the seven articles in this issue. The first three articles provide primers on topics (statistics to assess predictive accuracy, the Expected/Observed [E/O] Index, and mediation analyses, respectively) in a way that is meant to be understandable to clinicians and researchers. The next two articles describe and compare different statistics for assessing change over time. The last two articles explore limitations of currently used recidivism analyses (area under the curves [AUCs], Harrell’s C, Cox and logistic regression). We hope this issue will serve as a helpful resource for those who conduct or consume research on predicting recidivism.
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.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".