Correlates of the Victim–Offender Relationship in Homicide
Bibliographic record
Abstract
Using a classification of homicides based on the victim-offender relationship, this research analyzes individual-level data from a local prosecutor's office in Taiwan with multinomial logistic regression to locate the more precise correlates of three different homicide relationship types. The results of the analyses provide further support for the hypothesis that such partitioning of homicides is fruitful in revealing the relationships otherwise obscured. They indicate that both sociodemographic variables and situational variables are important correlates of three different homicide relationships, but their strengths vary based on the particular homicide relationship type. Age and crime premises correlate with homicide differently based on the victim-offender relationship. Premeditation is related to acquaintance homicide but not to intimate homicide. In contrast, previous conviction is associated with intimate homicide but not with acquaintance homicide. The implication of the findings is discussed within the limitation of the data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".