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Record W2123198796 · doi:10.1177/0093854808320169

Offender Coercion in Treatment

2008· article· en· W2123198796 on OpenAlexaff
Karen K. Parhar, J. Stephen Wormith, Dena Derkzen, Adele M. Beauregard

Bibliographic record

VenueCriminal Justice and Behavior · 2008
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRecidivismCoercion (linguistics)PsychologyCriminal justicePsychiatryTreatment and control groupsTurnoverInclusion (mineral)MedicineCriminologySocial psychology

Abstract

fetched live from OpenAlex

Mandating offenders to attend correctional treatment is a controversial function of the justice system, in part because of the uncertainty about the effectiveness of such practice. A meta-analysis was conducted to compare the effectiveness of mandated, coerced, and voluntary correctional treatment in reducing recidivism. A search of correctional treatment studies resulted in 129 studies meeting the inclusion criteria. In general, mandated treatment was found to be ineffective in several analyses, particularly when the treatment was located in custodial settings, whereas voluntary treatment produced significant treatment effect sizes regardless of setting. Few significant differences in effect sizes were found between levels of coercion. The implications of mandating correctional treatment for offenders 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 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)0.010
metaresearch head score (Gemma)0.052
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.052
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.120
GPT teacher head0.368
Teacher spread0.248 · 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

Citations188
Published2008
Admission routes1
Has abstractyes

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