Moralizing Supernatural Punishment and Reward
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
In this article we respond to three critiques of our 2019 article ‘Complex Societies Precede Moralizing Gods throughout World History.’ We clarify that our research does not, as our critics suppose, support the claim that moralizing gods played a decisive role in the development of complex societies. Indeed our goal was to test this claim and we found it wanting. Our methods ‘reduce’ neither religion or social complexity in the ways claimed, while our tentative conclusions about the relationship between frequent, routinized ritual and social cohesion are supported by much research beyond the paper under discussion. In the Roman Empire, many forms of collective ritual contributed to the propagation of Romanitas. We have never claimed that this depended on absolute uniformity of belief. Other misconceptions about our supposedly ‘inattentive’ qualitative analysis result from misreadings of information in our open-access database, which functions as an evolving set of information relevant to specific research questions rather than a general encyclopedia. Despite these disagreements, we continue to maintain that neither qualitative historical methods nor quantitative analytic approaches alone can produce satisfying answers to causal questions about world history. The best approach, we argue, is to integrate the insights from humanities with ‘Big Data’ analyses from social science, and we welcome continued engagement and collaboration across traditional disciplinary boundaries.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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