Emotional Congruence with Children: An Empirical Examination of Different Models in Men with a History of Sexually Offending Against Children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".