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Record W2224210367 · doi:10.1177/0093854815604011

An Examination of a Risk/Needs Assessment Instrument and Its Relation to Religiosity and Recidivism Among Probationers in a Muslim Culture

2015· article· en· W2224210367 on OpenAlexaff
Mazhar Hussain Bhutta, J. Stephen Wormith

Bibliographic record

VenueCriminal Justice and Behavior · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismReligiosityPsychologyRisk assessmentPersonalityScale (ratio)Clinical psychologySocial psychologyComputer securityGeography

Abstract

fetched live from OpenAlex

This study was conducted to assess the value of administering a risk/need assessment instrument to low-risk offenders in Pakistan. The Level of Service/Case Management Inventory (LS/CMI) and a measure of religiosity, the Muslim Religiosity-Personality Inventory (MRPI): Abridged Scale, were administered to probationers in this highly devout Muslim country that has little experience with risk/need assessment. In spite of the low recidivism rate, predictive validities based on correlation and receiver operating characteristic analyses were comparable with those of Western cultures overall, and for samples based on gender, geographic location, and type of crime. Although religiosity was negatively correlated with recidivism, it offered no incremental validity to the LS/CMI to predict recidivism because it was also correlated negatively with the LS/CMI. The findings have theoretical implications for the risk assessment of low-risk offenders and for the contribution of religiosity to offender risk and practical implications for the Pakistani justice system.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.659

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 science0.0000.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.358
Teacher spread0.298 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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