BRI and the Internationalization of China’s Renminbi
Bibliographic record
Abstract
Ever since the proposition of Belt and Road Initiative, known as BRI, by President Xi of China in 2013, it has accelerated the internationalization of RMB in many different ways, involving financing, investment and so forth. This study affirms the feasibility of using BRI to encourage RMB’s internationalization around the globe. The situations of related regions and Chinese corresponding endeavor have been discussed separately, for example, 17+1 cooperation achievement in transport sector in Europe, investments to Africa in large infrastructure, industrial, and communications projects, and taking advantage of Egypt’s “zero tariff” in the Middle East. Afterwards, the analysis of central bank currency swaps and points out the benefits offered by RMB’s internationalization for closing the infrastructure gap of BRI, and the BRI’s providing better opportunity to further the internationalization of RMB. Finally, both opportunities and challenges for RMB’s internationalization in related to BRI are discussed. This paper reveals that the internationalization of RMB via BRI is promising. Despite all kinds of challenges which are confronted by China, the country is given giant hope to handle well and convert those into opportunities, to encourage the use of RMB around the globe and make its contribution to world’s economy.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".