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Record W2164281273 · doi:10.5539/ach.v4n1p13

The Cultural Exchange between Sino-Western: Silk Trade in Han Dynasty

2011· article· en· W2164281273 on OpenAlexvenueno aff
Xiaoyan Wang, Jinsuo Zhao

Bibliographic record

VenueAsian Culture and History · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicChinese history and philosophy
Canadian institutionsnot available
FundersMinistry of Education, India
KeywordsChinaSILKGermanCultural exchangeAncient historyHistory of ChinaPoliticsBridge (graph theory)HistoryGeographyEconomyArchaeologyEngineeringPolitical scienceLawTelecommunications

Abstract

fetched live from OpenAlex

As we all know, the Silk Road, as a famous ancient transportation route, was a trade line cross-Eurasian continent in history. Its name was from the delivery of silk. However, no Chinese ancient documents mentioned the name of “Silk Road”. German F. V. Richthofen (1933-1905) firstly used the term “Silk Road” in his book China, published in 1877. Afterwards, the name of “Silk Road” has been accepted universally and used by the world widely. The Silk Road was an ancient business channel, acrossing the middle of China and countries in Central Asia, gradually forming after Qian Zhang visited Western Regions twice, two thousand and one hundred years ago. The north-west land Silk Road started from Chinese ancient Capital Chang’an (now Xi’an), acrossing Central Asia, and reaching ancient Rome in Europe. It was a bridge for communication of politics, economy, and culture between ancient China and the Western. Before 11 Century, the Sino-Western silk trade mainly depended on the land transportation. During Han Dynasty, it was a competition between the Huns and the Hans for occupy the Silk road. The silk as a kind of material culture was a sort of intermediary for making people to know how to get along together.This article attempts to describe the Sino-Western silk trade conditions before and after the two missions of Qian Zhang to Western Regions (Xiyu), including archaeological evidences, kinds of silk and trade scale, transportation routes, trade participants, and so on.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.942
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.060
GPT teacher head0.269
Teacher spread0.209 · 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 designNot applicable
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

Citations3
Published2011
Admission routes1
Has abstractyes

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