Research on the Export Potential of China’s Equipment Manufacturing Products to Countries Along the Belt and Road
Bibliographic record
Abstract
Based on China’s export trade data of equipment manufacturing to countries along the Belt and Road from 2006 to 2018, this paper uses a stochastic frontier gravity model to analyze the influencing factors and export trade efficiency of China’s export trade. The results indicate that: 1) Larger economic scale and population size, closer geographical distance, common boundaries and a common language can significantly increase China’s exports of equipment manufacturing products to countries along the route. 2) Signing free trade agreements with partners, increasing trade openness, and improving infrastructure construction can significantly improve China’s export trade level, while excessive tariff levels will inhibit export trade efficiency. 3) In recent years, the efficiency of China’s export trade to countries along the Belt and Road has improved, but the overall level is still low, and the efficiency of export trade to different countries varies greatly. Therefore, it is necessary to strengthen international trade cooperation, improve conditions for trade development, increase investment in infrastructure construction with countries along the Belt and Road, and adopt targeted strategies for different types of markets to develop market potential.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".