Bibliographic record
Abstract
The Central Bank Digital Currencies (CBDC), if appropriately designed, can help to improve the ‘Financial Inclusion’ in the Country.The Effective implementation needs a balanced, risk-based approach and the support to the complementary policies. The Central Banks in the World are actively considering, how the Retail Central Bank Digital Currencies (CBDCs) may fit with the Policy Goals around ‘Total Financial Inclusion’ in the Country. They are:~ Central Bank of The Bahamas.~ Bank of Canada.~ People’s Bank of China.~ Eastern Caribbean Central Bank.~ Bank of Ghana.~ Central Bank of Malaysia.~ Bangko Sentral ng Pilipinas.~ National Bank of Ukraine and~ Central Bank of Uruguay.While a Central Bank Digital Currency, like other forms of the Money, has different functions (e.g., Means of Payment, Store of Value, Unit of Account, Settlement Assets), its link to ‘Financial Inclusion’ in the Country in context of its payment properties.Abbreviations: CBDC= Central Bank Digital Currency.e-KYC= Electronic Know Your Customer.EMDEs=Emerging Markets and Developing Economies.AEs=Advanced Economies.PSPs=Payment Service Providers.ACHs=Automated Clearing Houses.
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 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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.099 |
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".