Making Fun of Animals: Ontological Implications of Rituals and Taboos Observed in Geographically and Linguistically Discontinuous Regions of Southeast Asia and Southwestern China
Bibliographic record
Abstract
ABSTRACT Forty years ago Robert Blust published a comprehensive, comparative analysis of what he called the ‘thunder complex’. Found among linguistically and culturally diverse populations in the Philippines, Indonesia, and peninsular Malaysia, the complex comprises a series of taboos and rites that centre on a belief that certain actions involving a confusion of categories will bring about a punitive storm and the death of offenders in resulting floods, landslides, or lightning strikes. The most typical and widespread of such taboos concern making fun of animals—for example, by dressing them in human clothes, talking to them, or otherwise making them appear ridiculous and so causing people to laugh. The present paper has three objectives. First, I identify a series of rituals performed by adherents of the complex that involve deliberately breaking taboos on animal mockery in order to produce needed rain. Secondly, I introduce a ceremony performed by ethnic minorities in southwestern China for the same purpose. The ceremony has all the hallmarks of the thunder complex and coexists with taboos on making fun of animals. Finally, I discuss what the complex, found among otherwise culturally and linguistic diverse societies, implies for their ontology in regard to human‐animal relations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".