Can We Distinguish Juvenile Violent Sex Offenders, Violent Non-sex Offenders, and Versatile Violent Sex Offenders Based on Childhood Risk Factors?
Bibliographic record
Abstract
Understanding the developmental precursors of juvenile violent sex offending can contribute to the promotion of effective early intervention and prevention programs for high-risk children and youth. However, there is currently a lack of research on the early characteristics of adolescents who commit violent sex offenses. Drawing on the literature regarding the generalist and specialist positions of criminal behavior, the aim of the present study was to compare childhood risk factors for three groups of juvenile offenders: (a) pure sex offenders (PSO; n = 28); (b) violent non-sex offenders (VNSO; n = 172); and (c) versatile violent sex offenders (VVSO; n = 24). Nineteen risk factors comprising four life domains (individual, family, peer, and school) were identified from a file review. Three hierarchical logistic regression analyses examined associations between risk factors and offender groups. The results reflected the underlying heterogeneity of the sample, offering support for both the specialist and generalist positions of criminal behavior. PSOs differed from VNSOs on the basis of higher odds for precocious sexual behavior. Second, VVSOs differed from VNSOs on the basis of higher odds for precocious sexual behavior, criminal family members, and an adolescent mother, as well as lower odds for poor school behavior. Third, PSOs were marginally more likely to have engaged in early overt antisocial behavior compared with VVSOs. Fourth, many of the childhood risk factors examined were not associated with any offender group. In conclusion, VVSOs appeared to differ on the greatest number of risk factors from VNSOs, suggesting that VVSOs share a more similar developmental pathway with PSOs. The prevention and future research implications of these findings are discussed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".