Animal Welfare and Policy Reforms for Korean Traditional Bull Fighting: Harmonizing Traditions with Animal Rights
Bibliographic record
Abstract
This study examines the welfare conditions, legal ambiguities, and economic inefficiencies inherent in Korea's traditional bullfighting practices. The study analyzes field data collected during 2025 (February-June), covering 131 contests across six venues, collected by the Animal Liberation Wave (ALW); the results reveal pervasive welfare violations, as evidenced by high avoidance (41.2%) and injury (62.3%) rates among bulls, alongside notable physiological and behavioral stress markers. From a legal perspective, the Animal Protection Act of Korea displays a core inconsistency: it prohibits cruelty to animals yet exempts bullfighting on cultural grounds, thereby compromising legal coherence and undermining welfare standards. Public opinion surveys further demonstrate declining societal support, particularly among younger and urban populations. Comparative cases from Spain, Mexico, and the European Union illustrate alternatives and possible reforms that may preserve cultural identity while eliminating harm against animals. Accordingly, this study proposes a phased policy framework comprising immediate welfare oversight, gradual redirection of subsidies toward humane cultural programs, and legislative amendment to remove the exemption clause. Ultimately, this study contends that harmonizing Korea's cultural heritage with international welfare norms is both ethically significant and legally necessary, offering a model for culturally sensitive reform in the global context.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".