The Myth of Offenders' Deception on Self-Report Measure Predicting Recidivism
Bibliographic record
Abstract
Two studies were conducted to investigate the vulnerability of the Self-Appraisal Questionnaire (SAQ) to deception and self-presentation biases. The SAQ is a self-report measure used to predict recidivism and guide institutional and program assignments. In the first study, comparisons were made between 429 volunteer offenders who completed the SAQ for research purposes and 75 offenders who completed the SAQ as a part of the psychological assessments process required for consideration for early release. In the second study, 106 participants over two sessions completed the SAQ and the Balanced Inventory of Desirable Responding. Participants completed both measures under two separate sets of instructions: (a) Answers would be used for research purposes, and (b) answers would be used for making decisions about their release to the community. Results from both studies reaffirmed previous findings that the SAQ is not vulnerable to deception, lying, and self-presentation biases.
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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 | 0.004 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".