The Unusual Case of Sexual Homicide Against Males: Comparisons and Classification
Bibliographic record
Abstract
This study examines the specificities of sexual homicides involving male victims. First, this study aims to identify characteristics specific to SH involving male victims by comparing them to SH involving female victims and determine whether rational choice approach and routine activities theories are useful to explain the crime-commission process. Second, this study aims to provide the first empirical classification of SH involving male victims. The sample used in this research comes from the Sexual Homicide International Database (SHIelD) including 662 cases of cases-100 cases involving male victims and 552 involving female victims. Bivariate and multivariate analysis are performed to examine the differences between the two groups and latent class analysis is used to generate an empirical classification of cases involving male victims. Findings indicate the victim's gender plays an important role in the different choices made by sexual homicide offenders of male victims to successfully complete their crime. They adapted their crime-commission process to overcome the risks associated with a physical confrontation with a male victim (i.e., target selection, approach strategy, method of killing). Classification analysis suggests that it exists three different types of sexual murderers assaulting male victims: the robber sexual murderer, the sadistic sexual murderer, and the pedophile murderer. This research proposes the first empirical typology of sexual homicide involving male victims and provides both a true picture of the reality and a comprehensive understanding of this phenomenon.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".