Screening offenders for risk of drop‐out and expulsion from correctional programmes
Bibliographic record
Abstract
Purpose. The goal of the present research was to develop a screening measure to assist in identifying offenders at risk for drop‐out or expulsion from correctional programmes. Methods. Non‐Aboriginal male offenders ( N = 5,247) were randomly divided into a development sample ( N = 2,617) and a validation sample ( N = 2,630). In the development sample, individual predictors were identified through univariate and multivariate analyses, weighted based on their relationship with drop‐out/expulsion, and combined into a composite measure we called the drop‐out risk screen (DRS). Results. The DRS consists of five items, including static and dynamic risk factors for recidivism as well as motivation for intervention. It significantly predicted drop‐out/expulsion in the development sample (area under the receiver operating characteristic curve [AUC]= .72) and performed similarly in the validation sample (AUC = .70). Conclusions. The results indicate that the DRS is a valid screening instrument for risk of drop‐out/expulsion. Prior to commencement of a treatment programme, offenders with high scores on the DRS could be more thoroughly assessed and, if necessary, targeted with pre‐treatment efforts to increase their motivation and general readiness for treatment.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".