Crime Scene Behaviors Indicate Risk-Relevant Propensities of Child Molesters
Bibliographic record
Abstract
The current study used crime scene analysis (CSA) to identify the psychological characteristics of child molesters and examined the contribution of these behavioral themes for sexual offender risk assessment. CSA was conducted on a sample of 424 cases of child sexual abuse in Berlin (Germany) using non-metric Multi-Dimensional Scaling. The analysis revealed the behavioral themes of fixation, regression (sexualization), criminality, and (sexualized) aggression, consistent with previous theories and empirical research in child molestation. The construct validity of the four themes was demonstrated through correlational analyses with known sexual offending measures, ratings of offender motivation, and criminal histories. The themes of fixation and (sexualized) aggression were significant predictors of sexual recidivism and added incrementally to the Static-99 for the prediction of sexual recidivism. The results indicate that crime scene information can inform the assessment of child molesters’ risk-relevant propensities and improve the prediction of sexual recidivism.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".