The Killing of Cousins and Siblings‐In‐Law in Korea: A Descriptive Study
Bibliographic record
Abstract
ABSTRACT The killing of cousins and siblings‐in‐law has been examined as part of ‘relative killings’ in prior research. However, they have not been disaggregated and examined in their own right. A content analysis of a major Korean newspaper (Chosun Ilbo) and sentencing verdicts from regional trial courts of original jurisdiction in Korea was conducted. This paper examines the offence characteristics in cousin and siblings‐in‐law killings. Cousin and siblings‐in‐law killings made up 4% of 682 family homicides. The victims and offenders were primarily men who used edged weapons to kill one another during the course of arguments. Women appeared as offenders and victims in the early period (1948–1962) while they appeared primarily as victims in the latter period (2013–2023). The data suggest a shift in the age structure of victims and putative motivations across time. The average age of victims and offenders increased by 20 years; the character of violence also shifted from confrontational homicides to killings in the context of domestic disputes between relatives' spouses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".