[Analysis and comparative study on international competitiveness of trditional Chinese medicine trade].
Bibliographic record
Abstract
Based on trditional Chinese medicine(TCM)trade data, spanning from 1996 to 2023, from UN Commodity Trade Statistics Database(UN Comtrad database), this study employed four evaluation metrics to analyze the current trade status of China's TCM and evolving trends of its international competitiveness in TCM trade. The evaluation metrics were international market share, trade specialization coefficient, revealed comparative advantage index, and composite trade competitiveness index. A comparative analysis was conducted with major trading nations including India, Canada, Germany, the United States, and Egypt. The study found that over 150 countries/regions participate in global TCM trade, with high-volume traders typically possessing either a long history of traditional medical practices or abundant medicinal resources. While China maintained the largest market share with expanding trade volume, its international competitiveness showed persistent decline, which indicated weakening comparative advantages. The United States, Germany, and France similarly exhibited declining competitiveness, while India and Egypt demonstrated significant competitiveness improvements. Notably, India had emerged as a key global player through rapid internationalization and standardization of its Ayurveda-based traditional medicine system. To optimize China's TCM trade strategy, this paper proposed exploring premium overseas medicinal resources, diversifying sourcing regions, and establishing international raw material bases to ensure stable supply chains.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".