Cross Validation of the Self-Appraisal Questionnaire (SAQ): A Tool for Assessing Violent and Nonviolent Recidivism in Australian Offenders
Bibliographic record
Abstract
The aim of this study was to determine whether the Self-Appraisal Questionnaire (SAQ; Loza, 1996), a self-report measure designed to predict recidivism, which was found to be psychometrically sound with Canadian male offenders, would also be reliable and valid for use with Australian male offenders. The SAQ consists of 72 items; with 6 subscales that measure offenders' criminogenic risk/need areas. The SAQ was administered to 116 male offenders incarcerated in rural southwestern Australia, along with the Psychopathy Checklist — Revised (PCL-R; Hare, 1991) and the Violence Risk Appraisal Guide (VRAG; Harris, Rice, & Quinsey, 1993). Data related to the offenders' criminal history were collected via review of institutional files. The Cronbach alphas for the SAQ subscale scores ranged from .68 to .76. The correlations between SAQ total score and subscale scores ranged from .48 to .86. The SAQ subscales significantly correlated with other instruments assessing recidivism. Offenders with high SAQ total scores had significantly more total number of offences, higher numbers of breaches of conditional releases, and higher numbers of violent offences. Offenders who committed violent offences scored significantly higher than those who committed nonviolent offences. These results support the previous findings establishing the reliability and validity of the SAQ for use with Canadian offenders and suggest that the SAQ may have applicability for use as an instrument for predicting violent and nonviolent recidivism in Australian populations. A follow-up predictive study is needed to further validate the SAQ on Australian offenders, and other offender populations, to widen its applicability.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".