Impact of cross-border e-commerce development on China’s foreign trade
Bibliographic record
Abstract
This study investigates the impact of cross-Border E-commerce development on China’s foreign trade. The software SPSS is used to calculate the value of each independent variable CBEC transaction volume, business infrastructure, professional talents, and development potential, and the software STATA version 18 is used to perform all the regression analyses. The findings reveal that efficient CBEC business infrastructure, including electronic payments, logistics, and digital support systems advancements, significantly enhances trade facilitation. Additionally, developing and cultivating professional CBEC talents are critical in sustaining trade growth, though there remains a significant talent gap in high-end, composite skills. Furthermore, the study highlights the immense potential of CBEC to broaden trade channels, improve global competitiveness, and foster innovation in small and medium-sized enterprises (SMEs). The analysis indicates steady growth in CBEC transactions and infrastructure, alongside an increasing internet penetration rate, supporting the sector's expansion. The study concludes with recommendations for policymakers and businesses, emphasizing the need to enhance infrastructure, cultivate professional talents, and strengthen market potential to ensure sustainable CBEC development and boost foreign trade. These insights provide a comprehensive understanding of the mechanisms CBEC influences foreign trade, offering a valuable reference for future research and policy formulation.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".