Explaining the rise of moralizing religions: a test of competing hypotheses using the Seshat Databank
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The causes, consequences, and timing of the rise of moralizing religions in world history have been the focus of intense debate. Progress has been limited by the availability of quantitative data to test competing theories, by divergent ideas regarding both predictor and outcomes variables, and by differences of opinion over methodology. To address all these problems, we utilize Seshat: Global History Databank, a large storehouse of information designed to test theories concerning the evolutionary drivers of social complexity. In addition to the Big Gods hypothesis, which proposes that moralizing religion contributed to the success of increasingly large-scale complex societies, we consider the role of warfare, animal husbandry, and agricultural productivity in the rise of moralizing religions. Using a broad range of new measures of belief in moralizing supernatural punishment, we find strong support for previous research showing that such beliefs did not drive the rise of social complexity. By contrast, our analyses indicate that intergroup warfare, supported by resource availability, played a major role in the evolution of both social complexity and moralizing religions. Thus, the correlation between social complexity and moralizing religion seems to result from shared evolutionary drivers, rather than from direct causal relationships between these two variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it