Historians Respond to Whitehouse et al. (2019), “Complex Societies Precede Moralizing Gods Throughout World History”
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
As historians, archaeologists, and database analysts affiliated with the Database of Religious History (DRH; religiondatabase.org), we share with the Seshat: Global History Databank team, authors of a recent study published in Nature, an excitement about the potential for deep and sustained collaborations between historians and analysts to answer big questions about human history. We have serious concerns, however, by the approach to the quantitative coding of historical data taken by the Seshat team, as revealed in the backing data (seshatdatabank.info/nature), as well as by a lack of clarity concerning the degree of involvement of expert historians in the coding process. The apparent lack of appreciation for historical scholarship that this coding strategy displays runs the risk of permanently alienating the community of academic historians, who are essential future collaborators in any project devoted to large-scale historical data analysis. In the present commentary, we present a preliminary critical review of their latest article, “Complex Societies Precede Moralizing Gods Throughout World History” (2019).
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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