Negotiating currency internationalization: An infrastructural analysis of the digital RMB
Bibliographic record
Abstract
Abstract In what ways might the digital renminbi (RMB), also known as e-CNY, bolster China’s efforts to internationalize its currency? Utilizing Susan Strange’s concept of currency negotiation and borrowing the concept of infrastructures from science, technology, and society studies, this article argues that RMB internationalization is a gradual process that relies heavily on negotiation involving both state and non-state actors (i.e., private financial authorities). It further argues that while e-CNY may create new opportunities for RMB internationalization, it also raises new challenges. First, the e-CNY’s lack of coordination with other central banks represents a challenge for future evolution and standardization with other digital currency platforms, thus rendering first-mover status a potential disadvantage. Second, as a result of China’s divergent data governance direction from both the US and the EU, the e-CNY is disadvantaged when it comes to interoperability, trust of users, and diversity of data. The purpose of this study is not to predict the future of RMB internationalization once the e-CNY rolls out but rather to highlight various ways in which the latter may influence the former in order to widen analyses of the topic.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".