Identifying victim types in sexual homicide: A latent class analysis using interactional victimology theories
Bibliographic record
Abstract
Sexual homicide (SH) research has focused on those who commit these crimes and the crimes themselves. This leaves the victim, an equally crucial piece to the puzzle, left as a sort of afterthought, despite the valuable insight that victimology provides to the crime. For the current study, victim information related to their routine activities and lifestyles was taken from an international database containing 662 solved cases of SH. Nine victim variables were used in a latent class analysis to find hidden subgroups within the victim population. Three classes were identified-low-risk, homebody, and overt risk victims-which suggests that SH victimization varies depending on the victim lifestyles and routine activities. These groups were externally validated by examining their association with different phases of the crime commission process. Some sexual homicide offenders may be more drawn to a victim because they present as vulnerable and opportunistic, while others might be methodically targeted. The theoretical relevance of this typology, along with investigative and prevention strategies, is 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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".