Bibliographic record
Abstract
The rural market was an important constituent of marketing system, and formed an un-vertical congruent relationship with urban market in late imperial China. There were different types of rural fair in the imperial China. Xu, Chang, Ji, Dian, Shi, Hui, all of them were the regular fairs. Their number was huge. They distributed widely, played a distinct role, and became the base of rural market development. During Tang and Song dynasties, county seat, town or village had some regular fairs. They were more and more developed during Yuan, Ming and Qing dynasties. In the late imperial China, the establishment or abolishment of rural regular fair must been approved by local magistrate, such as magistrate of a county. Equally important, the clan and Gentleman played the crucial role in rural market. On the whole, the network of rural fairs began to take shape in the most regions from Qianlong to Daoguang reigning years of the Qing Dynasty. The professional markets in rural society included two kinds: professional town and professional fair. The emergence of professional markets in rural society was the inevitable result of enlargement of cash crops planting and development of social division of labor, and helped in the shaping of specialized region which centered on cash farming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".