Revisiting the anger/sadistic typology of sexual homicide
Bibliographic record
Abstract
Purpose The anger/sadistic model is one of several typologies proposed for sexual homicide events. This paper aims to empirically test this model by examining sexual homicide cases. Empirically validating these typologies provides greater validity and reliability toward the sexual homicide classification systems that are useful in police investigations. Design/methodology/approach Secondary data analysis was conducted using police data on 249 solved sexual homicide cases in Canada from 1948 to 2010. Through a robust classifying method, latent class analysis was used to examine variables from the anger/sadistic typology. Additionally, variables from the pre-crime, crime and post-crime phases were examined in relation to the classes’ external validity. Findings Three classes emerged, namely, expressive, methodical and instrumental. Expressive and methodical were similar to the anger/sadistic model in terms of the presence of premeditation, victim-offender relationship and body disposal location. Instrumental was characterized by the absence of mutilation on the victim’s body, targeted acquaintances and the use of physical restraints. The three-class typology resembled evidence found in a previous systematic review and also reinforced the notion of heterogeneity in sexual homicide offenses. Originality/value This is the first study to empirically test the anger/sadistic typology. Such validation is important given that sexual homicide classification systems can aid in police investigations (e.g. narrowing down the list of potential suspects). Replication of studies is needed to lend credibility to research processes, which, in turn, allows practitioners and policymakers to integrate the results into policies effectively.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".