Bibliographic record
Abstract
Sedentary foraging is not identical to agriculture, which involves cultivation of plants and eventually their domestication. Cultivation appears to have developed in southwest Asia during a large negative climate shock called the Younger Dryas. After a prolonged period of warm and wet conditions during which regional population reached a high level, an abrupt reversion to colder and drier conditions forced this large regional population into a few high quality refuge sites where surface water was available from rivers, lakes, marshes, and springs. The resulting spike in local populations at these sites drove down the marginal product of labor in foraging and triggered reallocation of some labor toward cultivation. Once some populations adopted cultivation, learning by doing reinforced the incentive to engage in it. Eventually climate improved in the Holocene, regional population grew, and agriculture spread. We believe this mechanism accounts for the archaeological facts in the case of southwest Asia, and similar mechanisms might account for other pristine agricultural transitions (e.g., in China and sub-Saharan Africa). Our model clarifies the causal roles of climate, geography, technology, population, and migration in the development of pristine agriculture. It also helps explain why certain regions did not experience pristine agricultural transitions.
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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 | 0.000 | 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 | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".