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Record W2135051078 · doi:10.5539/ass.v6n6p42

The Rural Market in Late Imperial China

2010· article· en· W2135051078 on OpenAlexvenueno aff
Fang Ren

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Crisis of the 21st Century
Canadian institutionsnot available
FundersProgram for New Century Excellent Talents in University
KeywordsMagistrateChinaCashPeasantClanHistory of ChinaRural areaCash cropEconomic historyAgricultureEconomyEconomic growthGeographyHistoryPolitical scienceBusinessEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.595

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.216
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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