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Record W4391599581 · doi:10.1080/2833115x.2023.2273544

Central bank digital currencies and the future of monetary sovereignty

2024· article· en· W4391599581 on OpenAlexafffund
Colin Chia, Eric Helleiner

Bibliographic record

VenueFinance and Space · 2024
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsBalsillie School of International AffairsUniversity of WaterlooMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSovereigntyDigital currencyOpposition (politics)CurrencyEconomicsMonetary policyPoliticsMonetary baseState (computer science)Political economyMonetary economicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.189
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2024
Admission routes2
Has abstractyes

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