“Sorting Things out”: A Scoping Review of Sexual Homicide Typologies
Bibliographic record
Abstract
Sexual homicides are complex crimes that have been the focus of numerous classification systems aimed at aiding investigations, understanding offender behavior, and informing treatment plans. Over the past 25 years, a variety of typologies have been developed to categorize these offenses. This scoping review examines these typologies, exploring their evolution and the key offender, victim, and crime characteristics used to define them. The review identifies 19 empirical typologies from Canada, France, the UK, South Africa, and other regions, most of which are based on police and offender data. Typologies typically include categories such as "sadistic" and "anger-driven" homicides, though the number of types varies across studies. Moreover, the review highlights gaps in current research, such as limited sample sizes and the need for more diverse cultural perspectives. Recommendations are made for developing a more comprehensive and validated typology that incorporates broader data sources and modern methodologies, such as machine learning techniques, to enhance profiling, investigation, and prevention efforts.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.038 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".