Central bank digital currency: Advising the financial services industry
Bibliographic record
Abstract
The teaching case focuses on central bank digital currency, or CBDC, which would be a new kind of government-issued digital currency. Currently, money already flows around the world through electronic circuits. Private/non-government digital currencies such as cryptocurrencies are decentralised, unregulated and highly volatile. Unlike the private digital money, CBDC would be centralised and controlled digital money. CBDC would provide a stable means of exchange amongst the citizens and businesses as it would be controlled by the central bank and backed by the government. CBDC could be programmed, transferred and traced more easily and at a lower cost. Through this case, students will get the opportunity to understand the advantages, risks and challenges of CBDC and how CBDC is different from the existing digital currencies such as Bitcoin, stable-coins and Diem. The efficient integration of CBDC with existing banking and payment systems to ensure flawless operations is a vital success factor for a country to embrace CBDC. The digital system’s simplified administrative and regulatory requirements will also assist governments in significantly lowering operational and technology maintenance expenses. At the same time, a CBDC could threaten the commercial banks, allowing the government to communicate directly with CBDC holders. Through this case, students will learn about different models that can be used to implement and integrate the CBDC system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".