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Record W2095338611 · doi:10.1177/0886260504269180

Cross-Validation of the Self-Appraisal Questionnaire (SAQ)

2004· article· en· W2095338611 on OpenAlexaffabout
Wagdy Loza, Anita Cumbleton, Ariana Shahinfar, Lee Hong Neo, Maggie Evans, Michael Conley, Roger Summers

Bibliographic record

VenueJournal of Interpersonal Violence · 2004
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsQueen's University
Fundersnot available
KeywordsRecidivismPsychologyClinical psychologyConcurrent validitySuicide preventionSelf-report studyPoison controlReliability (semiconductor)PsychometricsPsychiatryMedicineInternal consistencyMedical emergency

Abstract

fetched live from OpenAlex

The Self-Appraisal Questionnaire (SAQ) is a 72-item self-report measure designed to predict violent and nonviolent recidivism among adult criminal offenders. The results from using samples from Australia, Canada, England, Singapore, and two samples from the United States (North Carolina and Pennsylvania) indicated that (a) the SAQ has sound psychometric properties, with acceptable reliability and concurrent validity for assessing recidivism and institutional adjustment; (b) there were no significant differences among the scores of the White, African American, Hispanic, and Aboriginal Australian offenders on the SAQ; (c) there were no significant differences among offenders who completed the SAQ for research purposes versus offenders who completed it as part of a decision-making process. Results provided support for the validity of the SAQ to be used with the culturally diverse offenders involved in this research and provided further evidence that contradicts concerns that the SAQ as a self-report measure may be susceptible to lying, and self-presentation biases.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.340
Teacher spread0.326 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2004
Admission routes2
Has abstractyes

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