Informing Large-Scale Emergent Decarceration Efforts: Validation of the Reduction in Capacity Evaluation (ReduCE)
Bibliographic record
Abstract
Large-scale emergent decarceration efforts introduce challenges for time and resources that prohibit jurisdictions from employing best practices in release decisions. Best practice includes a structured professional judgment approach by paroling authorities to incorporate group-level predicted risk and individual-level assessment of current risk and suitability for release. Absent the ability to expand parole review, jurisdictions should ensure decisions are informed by risk assessment that incorporates recent behavior and potential mitigating factors to approximate parole practice. The Reduction in Capacity Evaluation (ReduCE) was created for the California Department of Corrections and Rehabilitation following COVID-19 to inform prison population reduction efforts wherein empirically supported structured decision-making used by the Board of Parole Hearings cannot be employed. ReduCE is automatically scored, differentiates between lower risk groups, predicts any new return ( AUC = .69) and new felony return ( AUC = .75) across gender and race, and informs risk over and above the existing static instrument.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".