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Record W2140179522 · doi:10.1177/0306624x04273433

Cross-Validation of the Self-Appraisal Questionnaire: A Tool for Assessing Violent and Nonviolent Recidivism With Female Offenders

2005· article· en· W2140179522 on OpenAlexaff
Wagdy Loza, Lee Hong Neo, Ariana Shahinfar, Amel Loza-Fanous

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2005
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsKingston Health Sciences Centre
Fundersnot available
KeywordsRecidivismPsychologyPredictive validityClinical psychologyInjury preventionPoison controlHuman factors and ergonomicsSuicide preventionPsychiatryMedical emergencyMedicine

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 male criminal offenders. It was administered to 91 female offenders incarcerated in Pennsylvania and 183 incarcerated in Singapore correctional systems. Results indicated that the SAQ has sound psychometric properties, with acceptable reliability and concurrent and predictive validity for assessing violent and nonviolent recidivism. There were no significant differences between the scores of African American and Asian offenders and the responses of the White offenders. Similar to the findings from male offenders, the present results provide some support for the validity of the SAQ in the prediction of violent and nonviolent recidivism risk among White, African American, and Asian female offenders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.158
GPT teacher head0.411
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations47
Published2005
Admission routes1
Has abstractyes

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