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Record W4225517614 · doi:10.21428/cb6ab371.28cf57a8

Examining Antisocial Behavioral Antecedents of Juvenile Sexual Offenders and Juvenile Non-Sexual Offenders

2022· preprint· en· W4225517614 on OpenAlexaboutno aff
Evan McCuish, Patrick Lussier, Raymond R. Corrado

Bibliographic record

VenueCrimRxiv · 2022
Typepreprint
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyJuvenileCovertJuvenile delinquencyCategorizationDevelopmental psychologyAntisocial personality disorderClinical psychologyPoison controlInjury preventionMedicineMedical emergency

Abstract

fetched live from OpenAlex

Prior studies have indicated that that there is an “antisocial” type of juvenile sex offender (JSO) that resembles juvenile non-sex offenders (JNSOs). However, a single categorization of all antisocial JSOs may be too broad given that there are different types of antisocial behavior (e.g., authority conflict, overt, covert). To clarify potential differences between JSOs and JNSOs, different antisocial behavior patterns should be explored and compared between these two groups. This study examined data on Canadian male incarcerated adolescent offenders to identify whether behavioral antecedents differed within JSOs (n = 51), and between JSOs and JNSOs (n = 94). Latent class analysis identified three behavioral groups. For both JSOs and JNSOs there was a Low Antisocial, Overt, and Covert group. Risk factors including offence history, abuse history, and family history were more strongly associated with the Overt and Covert groups compared to the Low Antisocial group. Overall, there were important within-group differences in the behavioral patterns of JSOs, but these differences resembled differences within their JNSO counterpart. Clinical implications for responding to incarcerated JSOs with behavioral problems 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.003
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 head0.116
GPT teacher head0.365
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 teacher head, not a consensus.

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

Citations2
Published2022
Admission routes1
Has abstractyes

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