Cultural transmission and ecological opportunity jointly shaped global patterns of reliance on agriculture
Bibliographic record
Abstract
The evolution of agriculture improved food security and enabled significant increases in the size and complexity of human groups. Despite these positive effects, some societies never adopted these practices, became only partially reliant on them, or even reverted to foraging after temporarily adopting them. Given the critical importance of climate and biotic interactions for modern agriculture, it seems likely that ecological conditions could have played a major role in determining the degree to which different societies adopted farming. However, this seemingly simple proposition has been surprisingly difficult to prove and is currently controversial. Here, we investigate how recent agricultural practices relate both to contemporary ecological opportunities and the suitability of local environments for the first species domesticated by humans. Leveraging a globally distributed dataset on 1,291 traditional societies, we show that after accounting for the effects of cultural transmission and more current ecological opportunities, levels of reliance on farming continue to be predicted by the opportunities local ecologies provided to the first human domesticates even after centuries of cultural evolution. Based on the details of our models, we conclude that ecology probably helped shape the geography of agriculture by biasing both human movement and the human-assisted dispersal of domesticates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".