The Effectiveness of the Self-Appraisal Questionnaire in Predicting Offenders' Postrelease Outcome
Bibliographic record
Abstract
The goal of the present research was to examine the effectiveness of the Self-Appraisal Questionnaire (SAQ) in predicting release outcome as compared to other well-established risk prediction measures. The SAQ is a self-report measure designed to predict offenders' postrelease out-come. The SAQ was administered along with four similar, but clinician-administered, measures to 68 federally sentenced Canadian male offenders prior to their release into the community. Data were collected for a 2-year follow-up period at six 4-month intervals. Outcome criteria measures were general recidivism, violent recidivism, and any failure (a composite measure recording failure on any of the following variables: negative parole reports, violation of parole conditions, incurring new charges, or a new conviction). Although the SAQ was the most economical of the comparable tools, results demonstrated that it was at least as effective as the four other measures in predicting postrelease outcome.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".