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Record W4210975972 · doi:10.54691/bcpbm.v16i.244

BRI and the Internationalization of China’s Renminbi

2021· article· en· W4210975972 on OpenAlexaff
Xinran Hou, Shenyan Huang, Mengyue Liu, Peiyang Zhang

Bibliographic record

VenueBCP Business & Management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsBrock University
Fundersnot available
KeywordsRenminbiInternationalizationChinaGlobeBusinessCurrencyInvestment (military)TariffInternational economicsClosing (real estate)International tradeFinanceEconomicsExchange ratePolitical scienceMonetary economics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.192
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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