Magic and empiricism in early Chinese rainmaking -- A cultural evolutionary analysis
Bibliographic record
Abstract
Ritual protocols aimed at rainmaking have been a recurrent sociocultural phenomenon across societies and throughout history. Given the fact that such protocols were likely entirely ineffective, why did they repeatedly emerge and persist, sometimes over millennia even in populations with writing and record keeping? To address this puzzle, many scholars have argued that these protocols were not instrumental at all, and that their practitioners were not really endeavoring to employ them in order to bring about rain. Here, taking advantage of the wealth of historical records available in China, we argue to the contrary: that rainmaking is best viewed as an instrumental, means-end activity, and that people have always placed strong emphasis on the outcomes of such activities. To account for persistence of rainmaking, we then present a set of cultural evolutionary explanations, rooted in human psychology, that can explain why people’s adaptive learning processes did not result in the elimination of ineffective rainmaking methods. We suggest that a commitment to a supernatural worldview provides theoretical support for the plausibility of various rainmaking methods, and people often over-estimate the efficacy of rainmaking technologies because of statistical artefacts (some methods appear effective simply by chance) and under-reporting of disconfirmatory evidence (failures of rainmaking not reported/transmitted). The inclination to “do something” when a drought hits versus “do nothing” likely also plays a role and persists in the world today.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".