Motivation and crime scene behavior in Korean fire setting: a new typology
Bibliographic record
Abstract
Purpose Early classification systems of fire setting have suffered from several limitations, including the lack of empirical validation and the focus mainly on the offender motivation behind this type of crime. More recent research shows that looking at the crime scene behaviors may present a more fruitful approach for helping to solve fire setting offenses. The purpose of this study is to advance current scholarship by developing a new typology of fire setting based on the combination of offender motive and crime scene behaviors. Design/methodology/approach Latent class analyses were used with a sample of 134 fire setters who committed 275 arsons from the Korean National Police Agency to identify distinct fire setter motivations and crime scene contexts. Chi-square and crosstabulation analysis were then conducted to determine whether crime scene behaviors were associated with distinct offender motives and vice versa. Lastly, to improve the external validity of each of the latent classes, chi-square analyses were performed using variables related to the fire setters' criminal history, sociodemographic characteristics and arson classification. Findings Five motive subtypes were identified as well as five distinct crime scene contexts in which serial fire setting occurs. A significant association among these classes suggests that it is possible to infer fire setters’ motive from crime scene behavior and vice versa. Originality/value This comprehensive typology of fire setters has potential for profiling of unknown offenders as well as for suspect prioritization in police 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".