Predictive validity despite social desirability: evidence for the robustness of self‐report among offenders
Bibliographic record
Abstract
INTRODUCTION: Many professionals believe that self-report questionnaires used to predict recidivism have a low validity. The aim of the present study was to investigate the assumption that the validity of self-report is vulnerable to self-presentation biases in offender samples. METHOD: The participants consisted of 124 male offenders who volunteered to complete the Self-Appraisal Questionnaire (SAQ). RESULTS: Lower scores on measures of social desirability were significantly associated with higher levels of risk (as measured by self-report and a rated actuarial instrument) and a higher likelihood to re-offend. Further, stepwise regression analysis revealed that social desirability added significantly unique variance in the prediction of violent recidivism. DISCUSSION: The authors propose that impression management may be an enduring person-based characteristic within an offender sample rather than a situationally determined response style. The variance associated with this characterological information is proposed to be the source of the unique predictive variance.
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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 | 0.027 | 0.140 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".