Naked body disposal: an indicator of the type of sexual homicide
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate whether sexual homicide offenders (SHO) who dispose of the victim’s body naked present with particular crime scene characteristics. Design/methodology/approach This study aims to answer this question through the use of a sequential logistic regression to test the individual effects of each set of crime scene variables against the manner of disposal using a sample of 662 solved cases of extrafamilial sexual homicide from an international database. Findings Results demonstrated that the modus operandi behaviors of sexual penetration, asphyxiation, dismemberment and overkill were significantly associated with the body being disposed of naked. In addition, removing or destroying evidence from the scene was also significantly associated with a naked victim. In contrast, the body was more likely to be dumped clothed if the contact scene was deserted and the victim was a stranger. These results suggest that SHOs who dispose of the body naked are more in line with the sadistic sexual murderer, while clothed victims are often disposed of by angry offenders. Originality/value To the best of the authors’ knowledge, this is the first study to examine the particular manner of disposing the victim’s body naked in cases of sexual homicide.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".