A Journey to the West: The Ancient Dispersal of Rice Out of East Asia
Bibliographic record
Abstract
Rice is one of the most culturally valued and widely grown crops in the world today, and extensive research over the past decade has clarified much of the narrative of its domestication and early spread across East and South Asia. However, the timing and routes of its dispersal into West Asia and Europe, through which rice eventually became an important ingredient in global cuisines, has remained less clear. In this article, we discuss the piecemeal, but growing, archaeobotanical data for rice in West Asia. We also integrate written sources, linguistic data, and ethnohistoric analogies, in order to better understand the adoption of rice outside its regions of origin. The human-mediated westward spread of rice proceeded gradually, while its social standing and culinary uses repeatedly changing over time and place. Rice was present in West Asia and Europe by the tail end of the first millennium BC, but did not become a significant crop in West Asia until the past few centuries. Complementary historical, linguistic, and archaeobotanical data illustrate two separate and roughly contemporaneous routes of westward dispersal, one along the South Asian coast and the other through Silk Road trade. By better understanding the adoption of this water-demanding crop in the arid regions of West Asia, we explore an important chapter in human adaptation and agricultural decision making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".