Monitoring changes in risk of reoffending: A prospective study of 632 men on community supervision.
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
OBJECTIVES: Few studies have examined how much individuals change on intermediate targets of risk to reoffend. Even fewer studies have examined the extent to which change on such measures predict reoffending. Establishing the validity of intermediate measures requires a multistep approach that (a) assesses the reliability of the change, (b) assesses change using statistical analyses that can account for measurement error, and (c) examines the extent to which change on these intermediate measures predict reoffending. METHOD: = 632). RESULTS: We found that risk to reoffend changes across time, the pattern of change varies across individuals, risk levels can predict different patterns of change, and that the best predictors of recidivism are the latest score or a rolling average of scores. CONCLUSIONS: Community supervision can use recent information concerning the community adjustment of their clients to predict recidivism. Best practice includes updating assessments and adjusting supervision practices based on their clients' most recent assessment, or the average of previous assessments. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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