The Digital Loonie: The Legal Framework for a Central Bank Digital Currency in Canada and Beyond
Bibliographic record
Abstract
Central banks are increasingly contemplating the creation of digital sovereign currency. It would be legal tender, like the conventional coin and note, to preserve control over the financial sector and create a payment system that is flexible enough to adapt to social shifts. In developing, issuing, and maintaining such a central bank digital currency, however, central banks and other financial regulators face myriad legal challenges and considerations. As this chapter will show, most of the legislative framework needed for a central bank digital currency already exists in a Canadian context. However, significant amendments will be required to existing statutes. Canadian regulators must be concerned about money laundering, financing terrorism and fraud, investor protection, security, and privacy. Each of these legal issues will require legislative amendments and regulatory changes. Privacy and security are of particular concern, as individual consumers must trust central bank digital currencies as a reliable payment method. There will need to be international legal coordination and convergence on these legal issues. The legal challenges Canada works through in real time are similar to those explored by other central banks. This chapter will therefore be instructive for non-Canadian readers, drawing heavily on the international context.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.020 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".