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Record W3033180239 · doi:10.1177/0093854820925846

Assessment and Modification of General Criminal Attitudes Among Men Who Have Sexually Offended

2020· article· en· W3033180239 on OpenAlexaff
Mark E. Olver, Keira C. Stockdale, David J. Simourd

Bibliographic record

VenueCriminal Justice and Behavior · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismPsychologyPoison controlInjury preventionSuicide preventionHuman factors and ergonomicsClinical psychologyPsychiatryCriminal historyMedicineMedical emergency

Abstract

fetched live from OpenAlex

General criminal attitudes have been well established as a dynamic risk factor for the origin, maintenance, and continuation of criminal behavior. Guided by the risk–need–responsivity (RNR) framework, this study examined self-reported change on a measure of general criminal attitudes in a sample of incarcerated men who participated in a sexual offense treatment program. Participants were administered the original version of the Criminal Sentiments Scale (CSS) and other measures at pretreatment and posttreatment and followed up in the community an average 14 years post-release. The results demonstrated that CSS total and subscale scores predicted general and violent recidivism, showed convergence with actuarial measures of criminogenic need, and had clinically meaningful associations with responsivity considerations. Pre–post changes on the CSS were associated with decreased general and violent recidivism controlling for pretreatment score and baseline risk. Implications for forensic assessment and correctional intervention are discussed.

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)0.000
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 score0.221
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.132
GPT teacher head0.411
Teacher spread0.279 · 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

Citations4
Published2020
Admission routes1
Has abstractyes

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