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Record W2998697191 · doi:10.5539/ass.v16n1p35

Challenges for the Promotion and Development of Traditional Chinese Medicine in Central and Eastern Europe Under the Belt and Road Initiative

2019· article· en· W2998697191 on OpenAlexvenueno aff
Feifei Xue, HE Xiao-yong, Wenzhi Hao, Qin Jiajia, Jiaxu Chen

Bibliographic record

VenueAsian Social Science · 2019
Typearticle
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Studies
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of China
KeywordsConnotationPromotion (chess)Traditional Chinese medicineGovernment (linguistics)Political scienceWestern medicineQuality (philosophy)Traditional medicineMedicineBusinessAlternative medicineLinguisticsLawPathology

Abstract

fetched live from OpenAlex

Along with the implementation of the Belt and Road Initiative, traditional Chinese medicine (TCM) is increasingly used and attracts more interest in Central and Eastern Europe (CEE). As an important bridge between different cultures, translation plays a major role in promoting TCM in CEE. However, there are some problems in the translation process hindering further promotion of TCM theories and culture in CEE. First of all, the English translations of TCM classics and textbooks lack universally accepted standards, and the quality of TCM text translation is low. Secondly, TCM translators lack sufficient training in TCM knowledge. Also, the translation of TCM materials lacks cultural connotation. Through analyzing the current problems of TCM translation in CEE, this study proposed three suggestions: strengthening the exchange between the government and experts, regulating the translation of TCM textbooks, and strengthening the training of TCM translators.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0070.005
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.093
GPT teacher head0.323
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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