Choice of Weapon or Weapon of Choice? Examining the Interactions between Victim Characteristics in Single‐victim Male Sexual Homicide Offenders
Bibliographic record
Abstract
Abstract As most studies report that the majority of sexual homicide offenders (SHOs) prefer to kill with their own hands, research has largely neglected to examine the choice of weapon by these offenders. The US Supplementary Homicide Reports show that although a large number of SHOs murder their victim using personal weapons (e.g. bare hands and manual or ligature strangulation), the majority use an alternative weapon (e.g. edged weapons, contact weapons, and firearms). The present study hypothesises that the choice of weapon is in part influenced by victim characteristics. To identify specific combinations and interactions between victim characteristics and the choice of a personal or edged weapon during the commission of a sexual homicide, a combination of exhaustive chi‐square automatic interaction detector and conjunctive analysis is used on a sample of 2,472 single‐victim male SHOs from a 36‐year period of Supplementary Homicide Report data (1976–2011). Findings show that SHOs choose their weapon according to some victim characteristics. Implications of the findings are discussed in light of police suspect prioritisation. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".