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Record W2044512101 · doi:10.1177/1079063214535814

Victim Age and the Generalist Versus Specialist Distinction in Adolescent Sexual Offending

2014· article· en· W2044512101 on OpenAlexaff
Elisabeth J. Leroux, Lesleigh E. Pullman, Gregory Motayne, Michael C. Seto

Bibliographic record

VenueSexual Abuse · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsUniversity of OttawaRoyal Ottawa Mental Health CentreToronto Metropolitan University
Fundersnot available
KeywordsPsychologyJuvenile delinquencySexual abuseCompetence (human resources)EtiologyDevelopmental psychologyClinical psychologyInjury preventionPsychiatryPoison controlMedicineSocial psychologyMedical emergency

Abstract

fetched live from OpenAlex

More knowledge is needed about the etiology and treatment needs of adolescent sex offenders. The current study compared adolescents who had offended against children (defined as below the age of 12 and at least 5 years younger than the adolescent), adolescents who have offended against peers or adults, and adolescents who had victims in both age groups. Based on Seto and Lalumière's meta-analytic findings, participants were compared on theoretically derived factors, including childhood sexual abuse, atypical sexual interests, sexual experience, social competence, psychiatric history, and general delinquency factors (past criminal history, substance abuse history, and offense characteristics). The study sample consisted of 162 court-referred male adolescent sexual offenders aged 12 to 17 years. Of the six identified domains, groups significantly differed on five of them; the exceptions were variables reflecting social competence. The results further support the validity of distinguishing adolescent sex offenders by victim age.

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)0.011
metaresearch head score (Gemma)0.023
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 score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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

Citations48
Published2014
Admission routes1
Has abstractyes

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