Body Disposal: Spatial and Temporal Characteristics in Korean Homicide
Bibliographic record
Abstract
This study explores the body disposal patterns in a sample of 54 Korean homicides that occurred between 2006 and 2012. Based on information collected by the police during their investigation, factors that could influence body disposal patterns were examined, such as homicide classification, intention, whether an accomplice was present, and offender mental disorder. Bivariate analyses showed that the majority of the victims who were disposed of were acquaintances of the offenders. Moreover, several offenders were more likely to dispose of the dead body "within hours" of killing the victim. Dead bodies were usually recovered in agricultural areas, forest/wooded areas, as well as residential areas. It was also noteworthy that, in 47 cases, the offender had knowledge of the geographic area where the body was dumped. In cases of "expressive" homicide, victims were more likely to be disposed of somewhere far away (e.g., over 40 km) from the crime scene, whereas "instrumental" homicide victims appeared to be disposed of somewhere closer (e.g., within 30 km) to the crime scene. Results are discussed in light of their practical implications for homicide investigations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".