Sadism in sexual homicide offenders: identifying distinct groups
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether it is possible to identify different types of sadistic offenders within a sample of sexual homicide offenders (SHOs). Design/methodology/approach The study addresses this research question through the use of two-step hierarchal cluster analysis and binary logistic regression utilizing a sample of 350 cases of sexual homicide from Canada. Findings Results from cluster analysis show that three groups emerge: a non-sadistic group, a mixed group that show evidence of some sadistic behavior and a sadistic group that have high levels of sadistic behavior. Additionally, the sadistic cluster was more likely to destroy or remove evidence at the crime scene than the mixed and non-sadistic cluster and was more likely to leave the victim’s body at a deserted location than the non-sadistic cluster. Originality/value This is the first study to examine the dimensionality of sadism within a sample of SHOs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".