Testing the Big Gods hypothesis with global historical data: a review and “retake”
Bibliographic record
Abstract
This Retake article presents a corrected and extended version of a Letter published in Nature (Whitehouse et al., 2019) which set out to test the Big Gods hypothesis proposing that beliefs in moralizing punitive deities drove the evolution of sociopolitical complexity in world history. The Letter was retracted by the authors in response to a critique by Beheim et al. (2021). Correction of errors in the coding and analysis of missing data to address this critique does not, however, significantly change the main findings of the original Nature Letter. We report the results of a major reanalysis of Seshat data following expansion of the codebook and database and substantial improvements to our data management methods. We also employ a more direct statistical methodology to test theories of evolutionary causality. Together, these results show a compellingly convergent picture, confirming the headline finding of the original Letter in Nature, which shows that the largest increases in social complexity do indeed precede Big Gods in world history and that Big Gods did not contribute to the evolution of sociopolitical complexity as predicted by the Big Gods hypothesis.
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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 | 0.030 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".