Tell Me What You Do, I’ll Tell You Who You Are: Predicting Offender-Victim Relationships in Sexual Homicide
Bibliographic record
Abstract
This study fills a gap in the existing literature by differentiating between sexual homicides committed by strangers and those by acquaintances. Utilizing data from the Sexual Homicide International Database, which encompasses 772 cases from France and Canada, the research focuses on using victimological and crime scene characteristics to predict the victim-offender relationship in sexual homicides. Employing a comprehensive methodological approach, the study uses bivariate analysis, sequential binary logistic regression, and an artificial neural network (ANN) model. These methods help in examining the correlations and predictive values of various factors in determining the nature of the victim-offender relationship. The findings highlight significant differences in the modus operandi of stranger and acquaintance offenders. Stranger offenders are more likely to exhibit violent, premeditated actions involving weapons, while acquaintance offenders tend to use verbal aggression, exploiting their existing relationship with the victim. Theoretically, results provide empirical insights into the dynamics of sexual homicides, expanding the understanding of offender behavior and crime scene analysis. Practically, it offers valuable guidance for law enforcement in criminal investigations and resource allocation.
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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 | 0.012 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".