Bibliographic record
Abstract
Silk Road developments increased interconnectivity through trade, but little is written about the resulting effect on food diversity. I used three methodologically, geographically and temporally diverse studies examining aspects of food during the Silk Road period to identify key factors affecting botanical and dietary food diversification in Central Asia during the first millennium. Archaeological and historical data from a study of Tashbulak (800-1100) revealed narrowing of genetic diversity accompanying cultivation, but also broadening of food options through trade and human interventions that created new plant varieties. A comparative study of the medieval period (500-1300) using human remains and published isotopic (δ13C and δ15N) records of urban and non-urban consumers in the Turkmenistan-Uzbekistan-Kazakhstan region showed the Silk Road fostered greater overall food diversity than occurred in the Iron Age and early first millennium (1300 BCE- 600 CE). It also showed that, although during the medieval period enhanced trade opportunities facilitated a food-diversity trend, the positive movement was eroded by urban, insular agricultural communities with reified social structures. Foodways analysis of recipe books revealed that during the Mongol period (1200-1400), multi-cultural interaction enhanced dietary diversity, whereas changing power dynamics, tradition, and sense of place countered the trend. The Silk Road was not a unilinear path toward dietary diversity, but rather, a series of winding routes beset with potentially precarious switchbacks. Travelling back along the first millennium Silk Road uncovers critical turning points that can inform global food diversity approaches in the 21st century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".