The e-Banknote as a ‘Banknote’: A Monetary Law Interpreted
Bibliographic record
Abstract
Abstract This article discusses whether electronic banknotes can properly be characterised as ‘banknotes’, their design as such and what architectural models are available for their issuance by central banks. Issues are addressed under general principles governing the interpretation of monetary laws in the context of evolving technologies and institutional arrangements. The article proposes a clear terminology to address the concepts underlying digital currencies and access to central bank money, and argues that a banknote may be ‘written’ electronically. The stance adopted in the article is critical of both account-based retail central bank digital currency and cryptocurrencies, and highlights the salient features of non-blockchain token-based alternatives. It sheds light on the considerations that influence the selection of a design that is appropriate from both the functional and legal perspectives.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".