Central bank digital currency: Practice in some countries and recommendations to Vietnam
Bibliographic record
Abstract
Digital currency (DC) has attracted strong attention in recent years. It has the potential to be widely used in payment and settlement. The governments and central banks around the world are closely monitoring the development of digital currencies. A number of countries (England, Sweden, Canada, India, China, Thailand, Philippines...) are also conducting in-depth research on the central bank digital currency (CBDC) and its impact on operating the monetary policy, the financial system- banking and payment activities in the economy. An important policy question for the central banks is whether the digital currency should be issued by a central bank to be used in the payment system. This article will clearly provide the definition as well as the function of CBDCs by collecting information and data through official announcements or reports of each country’s central bank, which may be helpful for policy markers’s Vietnam in monitoring the financial system in the technology era. In addition, the article also critiques the studies and assessments of central banks in a number of countries and finds that in Vietnam, although the government has been making efforts to improve the legal framework, due to the lack of technological infrastructure leads to the inability to maximize the efficiency of resources to improve financial stability. Therefore, Vietnam has continued to review and establish the legal framework for development of digital currencies and quickly built a strong technological infrastructure.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".