Combining historical and archaeological data with crop models to estimate agricultural productivity in past societies
Bibliographic record
Abstract
Carrying capacity, population pressure, and agricultural productivity are of central importance to understanding key innovations in human social and cultural evolution. In this paper we outline how crop yield models can be combined with the historical and archaeological information about past societies compiled by Seshat: Global History Database to infer how agricultural productivity and potential have changed over time in different parts of the world. To aid comparative research we focus on developing a method for calculating the carrying capacity of a particular region based on a number of simplifying assumptions. Here we present two case studies demonstrating the calculation of ancient crop yields and carrying capacity for the regions of Latium (Italy) and Oaxaca (Mexico); regions selected to illustrate a number of different features of past agricultural systems, as well as different staple crops. We outline the strengths and weaknesses of this approach and discuss ways in which it could be adapted to address a range of research questions, e.g. relating to archaeological demography and anthropogenic change. Comparison of our reconstructed carrying capacity series with independent estimates of ancient population from these regions demonstrate broadly good agreement with some notable mismatches as well, highlighting a fruitful area of focus for future studies exploring the gap between achieved population and potential carrying capacity.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".