Treatment of missing data determined conclusions regarding moralizing gods
Bibliographic record
Abstract
Whitehouse, et al. 1 have recently used the Seshat archaeo-historical databank 2 to argue that beliefs in moralizing gods appear in world history only after the formation of complex "megasocieties" of around one million people.Inspection of the authors' data, however, shows that 61% of Seshat data points on moralizing gods are missing values, mostly from smaller populations below one million people, and during the analysis the authors re-coded these data points to signify the absence of moralizing gods beliefs.When we confine the analysis only to the extant data or use various standard imputation methods, the reported finding is reversed: moralizing gods precede increases in social complexity.We suggest that the reported "megasociety threshold" for the emergence of moralizing gods is thus solely a consequence of the decision to re-code nearly two-thirds of Seshat data from unknown values to known absences of moralizing gods.Table 1 illustrates the Seshat data for the 12 key world regions used in the authors' t-tests.What the authors describe as the "first appearance" of moralizing gods only occurs after writing or literate observers (Extended Data Figure 1), and is almost always preceded by a series of unknown values ('NA's), indicating no evidence that moralizing gods were present or absent (Table 1).Only one observation in the entire Seshat database--China's Middle Yellow River Valley--reports a known absence of moralizing gods in a world region before their "first appearance", and this lone data point is disputed by other historians 3 .
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".