Supernatural agents and prosociality in historical China: micro-modeling the cultural evolution of gods and morality in textual corpora
Bibliographic record
Abstract
A major source of attention paid to high gods in the fields of cultural evolution and cognitive science is the social effects of belief in high gods. Belief in high gods is both hypothesized to catalyze a cognitive punishment-avoidance mechanism at the level of individual minds, and a group cultural evolutionary mechanism that amplifies in-group cooperation. Recent research into non-Western contexts not only indicates a multiplicity of supernatural influences on the individual-level and group-level mechanisms but raises questions about theoretical presuppositions about how a supernatural agent is classified as a high god or as something else. Our exploratory study operationalizes the question “Does historical China have high gods?” through the assessment of semantic associations between each of several supernatural agent categories (alleged high gods, low gods, ancestors, sage kings, and emperors) and each of several social functional content categories (punishment, reward, morality, monitoring, and religion). Analyzing collocations in a corpus of 5.7 m Chinese characters, representing all of the most influential historical Chinese-language texts, our preliminary results suggest social functions of supernatural agents in historical China were widely distributed across many species of supernatural agent thereby complicating a claim that high gods constitute a special category in relation to these social functions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".