MétaCan
Menu
Back to cohort
Record W2059119969 · doi:10.1177/0306624x00446007

Gender Difference in Mentally Ill Offenders: A Nationwide Japanese Study

2000· article· en· W2059119969 on OpenAlexaff
Liya Xie

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2000
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychiatryMentally illSchizophrenia (object-oriented programming)PsychologyPopulationDepression (economics)Substance abusePersonality disordersHomicideClinical psychologySuicide preventionPoison controlMedicinePersonalityMental illnessMental healthMedical emergency

Abstract

fetched live from OpenAlex

The entire population of 2,094 mentally ill offenders who were adjudicated as partially or fully not criminally responsible on account of mental disorders during the years of 1980 and 1994 throughout Japan were studied. Men were predominant. More than 60% of the participants had previously received psychiatric treatment. Schizophrenia and other psychoses were the most common diagnoses among both males and females. Females were more likely to be charged with violent crimes, and half of them committed homicide. Females attacked family members more often, and they were diagnosed with depression more often than were males. In contrast, males were more often charged with nonviolent crimes and had a greater number of criminal records. Despite the fact that persons diagnosed solely with personality disorders were largely excluded from the study, male mentally ill offenders still shared more negative demographic factors with male criminals in general, such as being unmarried, having a lower educational level, a poorer employment history, chaotic lives, and substance abuse problems.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.020
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.298
GPT teacher head0.398
Teacher spread0.100 · 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

Citations8
Published2000
Admission routes1
Has abstractyes

Explore more

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