Sadistic Sexual Crimes Against Children: Comparing the Manifestation of Sexual Sadism and Crime-Commission Process
Bibliographic record
Abstract
Individuals who commit sexual offenses against children have been shown to be distinctive from adult offenders across both individual and crime characteristics. However, an examination of the literature shows that there are gaps in the research related to whether sadism manifests differently between those who target child compared to adult victims. The current study therefore aimed to explore the differences in the characteristics, crime-commission process, and the dimensions of sadism between sadistic crimes of children ( n = 101) compared to those of adults ( n = 433). Sexual sadism was assessed with the Sexual Sadism Scale (SeSaS) and binary logistic regression analysis and multidimensional scale analysis (MDS) were performed to examine differences between these two groups. Our results showed that sadistic fantasies manifested into four dimensions for both adult and child victims. In terms of differences, sadistic sexual offenses involving children appear to be reflective of deviant fantasies related to overlapping paraphilias (e.g., sadism and pedophilia). Conversely, sadistic crimes involving adult victims involve a crime-commission process that involves a greater degree of structure to control their adult victim and decrease their risk of identification. Implications for clinical assessment and police investigations 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".