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Record W1971805167 · doi:10.1177/0306624x04263867

Criminal Justice Institutional Referrals and Selections: a Comparative Portrait of Sexual Aggressions and Aggressors

2004· article· en· W1971805167 on OpenAlexaff
Jean‐Pierre Guay, Marc Ouimet, Jean Proulx

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2004
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversité de MontréalInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsSeriousnessCriminal justicePsychologyCriminologySample (material)Sexual assaultEconomic JusticeInstitutionHuman factors and ergonomicsPoison controlMedicinePolitical scienceLawMedical emergency

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the judicial treatment of sex offenders from police detection to treatment centers. Using three different samples, participants' trajectories are studied in the light of their age, the age and sex of their victims, the relationship with their victims, and the use of a weapon. First, the results show that although underrepresented at the federal institution, younger criminals tend to be overrepresented in the treatment sample. Second, the results also demonstrate that offenders against children tend to be overrepresented at the federal institution; this tendency is even stronger in the psychiatric treatment sample. Third, the objective seriousness of the offense, a proxy measured by the presence of a weapon, is of principal importance in case processing throughout the judicial system. Recommendations on how to facilitate the comparison of results from different studies, based on a better sample description, are also 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)0.001
metaresearch head score (Gemma)0.006
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.365
GPT teacher head0.421
Teacher spread0.056 · 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

Citations10
Published2004
Admission routes1
Has abstractyes

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Same venueInternational Journal of Offender Therapy and Comparative CriminologySame topicPsychopathy, Forensic Psychiatry, Sexual OffendingFrench-language works237,207