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Record W6927861143 · doi:10.34989/sdp-2021-17

Revisiting the Monetary Sovereignty Rationale for CBDCs

2021· article· en· W6927861143 on OpenAlexaff

Bibliographic record

VenueEconstor (Econstor) · 2021
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCurrency substitutionCurrencyMonetary policyArgument (complex analysis)SovereigntyDigital currencyMonetary baseCryptocurrency

Abstract

fetched live from OpenAlex

As currently articulated, the monetary sovereignty argument for central bank digital currencies (CBDCs) rests on the idea that without them, private and foreign digital monies could displace domestic currencies (a process called currency substitution), threatening the central bank’s monetary policy and lender-of-last-resort (LLR) capabilities. This rationale provides a crucial but incomplete picture of what is at stake in terms of monetary sovereignty. This paper seeks to expand and enhance this picture in three ways. The first is by looking at the consequences of currency substitution that go beyond the functions of a central bank—important considerations that have received less attention in public CBDC discussions. The second is by exploring key differences in monetary policy and LLR capabilities across currency-issuing countries or regions. More specifically, the paper highlights the variation in the degree of monetary sovereignty and the consequences that different countries face should they lose it. The third way is by assessing not only the implications but also the risks of currency substitution and showing how these are also likely to vary across countries. Contrasting the consequences and risks of substitution, the paper concludes by noting a potential inverse relationship between the impact and probability of losing monetary sovereignty.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.229
Teacher spread0.215 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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