Assessing the global value chain trade structure of the EU, RCEP and TPP through trade network analysis
Bibliographic record
Abstract
Using network analysis, the study examines the trade structure of the three largest trade blocs, the European Union (EU), the Regional Comprehensive Economic Partnership (RCEP) and the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP). The use of intermediate goods exports data from the OECD TIVA database to assess the trade flow among member countries. It employs centrality measures such as centrality degree, eigenvector, betweenness, and closeness to identify the complex network flow of trade and the extent of trade concentration to countries within each bloc. The results reveal that the EU and RCEP supply chains are dominated by Germany and China, respectively. However, other countries are also acting as a central hub in the EU bloc. Similarly, the CPTPP supply chain is governed by nations such as South Korea, Japan, and Canada. Overall, the EU and RCEP have a dense trade network where countries have deep integration for efficient trade flow. In contrast, in CPTPP, the developed countries have higher participation, and the underdeveloped countries have less participation in trade flow. The findings provide important implications that high dependence on the central hub potentially poses the vulnerability of external/ internal shock in regional trade partnerships. Furthermore, high-quality standards favour developed nations and hinder underdeveloped nations' participation in regional trade partnerships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".