Central bank digital currencies and the future of monetary sovereignty
Bibliographic record
Abstract
Initiatives to develop central bank digital currencies (CBDCs) have accelerated dramatically across the world in the last few years. What is their significance for longstanding scholarly debates about the fate of monetary sovereignty in the digital age? Public officials themselves state that a central reason for these initiatives is to defend monetary sovereignty against threats emanating from the growth of private digital currencies, foreign CBDCs, and the displacement of state issued cash by private digital payments instruments. They (and others) highlight how CBDCs could even strengthen monetary sovereignty by bolstering financial inclusion as well as enhancing the state’s capacity to monitor and control monetary transactions and conduct monetary policy. In these ways, CBDC initiatives cast doubt on arguments that suggest the digital currency revolution necessarily challenges monetary sovereignty. However, critics of that line of argument also need to be cautious because CBDCs are already attracting much political opposition. Even if that opposition is overcome, CBDCs may be implemented in constrained ways or be unsuccessful in meeting their goals for other reasons. In short, CBDCs may hold the potential to defend and even strengthen monetary sovereignty, but it is far from clear whether this potential will be realised.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".