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Record W2091908085 · doi:10.1177/0093854814521415

Measuring Antisocial Values and Attitudes in Justice-Involved Male Youth

2014· article· en· W2091908085 on OpenAlexaff
Tracey A. Skilling, Geoff B. Sorge

Bibliographic record

VenueCriminal Justice and Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsYork UniversityUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsJuvenile delinquencyPsychologyPrideRecidivismScale (ratio)Criminal justicePoison controlHuman factors and ergonomicsSample (material)Injury preventionConstruct (python library)Social psychologyClinical psychologyDevelopmental psychologyCriminologyMedicineMedical emergencyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The Risk–Need–Responsivity (RNR) framework for working with offenders has been well validated. Factors that contribute to reoffending within adult and youth forensic populations have been identified, including antisocial attitudes, but less is known about the measurement of this construct in youth. Thus, in the present study, the reliability and validity of criminal attitudes measures were examined in a sample of justice-involved male youth ( N = 291). Two measures widely used with adult offenders were included in the present study: the Pride in Delinquency Scale (PID) and the Criminal Sentiments Scale–Modified (CSS-M). Both measures were found to be reliable and valid, and of importance, useful in the prediction of reoffending behavior (area under the curve = .70 and .69 respectively). These findings further support the use of the RNR framework in general with youthful offenders, and more specifically, the use of criminal attitudes measures with youth.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.109
GPT teacher head0.351
Teacher spread0.242 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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