Foreign Object Insertion in Sexual Homicide: A New Perspective
Bibliographic record
Abstract
Foreign object insertion (FOI) is considered as an unusual behavior and has been defined as the involuntary insertion of any object, by another individual, into any orifice of the victim. Although there is some research on the prevalence and nature of FOI in sexual homicides, there is very little on the characteristics of cases where FOI occurs, and no previous research has compared cases with and without FOI. Given the lack of research on FOI in general and the dissemination of untested ideas regarding the correlates of this behavior specifically, the current study aims to shine new light on sexual homicide cases involving FOI by examining the offender, victim, and crime characteristics associated with FOI. Using a sample of 662 cases of sexual homicide, chi-square and logistic regression analyses were used to compare cases with and without FOI. Results showed that offenders who experienced sexual dysfunction and victims who used alcohol/drugs prior to the crime were more likely to be involved in cases with FOI. Cases where victims were beaten, vaginal/anal fisting acts were perpetrated, and mutilation of genitals were observed, were more likely to show evidence of FOI. Finally, postmortem sexual activities and the use of strategies by offenders to avoid police detection were also more likely to occur in sexual homicide cases characterized by FOI. These findings are discussed in light of the literature on sexual homicide, the vulnerability of victims, and the manifestation of sadism. Practical implications are also discussed.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".