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Record W4379375394 · doi:10.1177/10790632231172160

Emotional Congruence with Children: An Empirical Examination of Different Models in Men with a History of Sexually Offending Against Children

2023· article· en· W4379375394 on OpenAlexaff
Julia M. Fraser, Kelly M. Babchishin, L. Maaike Helmus

Bibliographic record

VenueSexual Abuse · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser UniversityCarleton University
Fundersnot available
KeywordsPsychologyLatent class modelRecidivismLonelinessStructural equation modelingDevelopmental psychologyCongruence (geometry)Clinical psychologySocial psychology

Abstract

fetched live from OpenAlex

Emotional congruence with children (ECWC) is a psychologically meaningful risk factor for sexual offending against children (SOC). Based on previous research and theory, three models have been proposed to explain ECWC: Blockage, Sexual Domain, and Psychological Immaturity. Using structural equation modelling in a routine correctional sample of men adjudicated for sexual offences ( n = 983), we found little support for all three of these models. Instead, we found that atypical sexual interests, alone, best explained ECWC, with a moderate relationship to ECWC. Using the predictors associated with each of the three models of ECWC, we identified three classes of men with a history of SOC who are high in ECWC using latent class analyses ( n = 377). These three classes generally did not replicate the three models of ECWC. We instead propose three subgroups of men with histories of SOC who are high in ECWC, characterized respectively by: relationship deficits; youth and loneliness; and high sexual and general criminality. High levels of ECWC are predictive of a higher risk of sexual recidivism, regardless of class association; however, these subgroups are differentially at risk for some types of recidivism. Our findings suggest that ECWC is a multi-faceted construct, which is still not well understood.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.097
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.038
GPT teacher head0.294
Teacher spread0.255 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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