Rising Role of China in the Asia-Pacific Interconnected Economies: Current Status and Future Opportunities
Bibliographic record
Abstract
This paper investigates the rising role of China in the Asia-Pacific interconnected economy. This research utilizes export data to analyze, includes the quarterly international export data of Australia and Japan with its partner countries, the export data with China, Japan and the US as partner country with the typical countries include Canada, Mexico, USA, Australia, Japan, Chile, New Zealand, China and Indonesia. The results indicate that a) the significant role of China in international GVCs and in Asia-Pacific interconnected economy, b) the flourishing transaction value between these three years indicates the rising role of China all over the world, c) the essential position of the Chinese economy in the Asian economy, the joint Asia-Pacific economy, the potential of China as a developing country economy and the strength of its domestic industries. As for managerial implications, China is recommended to seize the initiative, expand its influence and insist on the development of Belt and Road strategy. The limitation of this study is that the diversity of observations. In a broader sense, more research is required to determine the universality or internationalization of the study subjects and comparisons across numerous time periods. Additionally, some industries may have an impact on each nation's export transaction values; this is a topic that merits additional study and application.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".