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Record W4407526210 · doi:10.3390/jrfm18020098

Understanding the Future of Money: The Struggle Between Government Control and Decentralization

2025· article· en· W4407526210 on OpenAlexvenueno aff
Jodi Tommerdahl

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsDecentralizationGovernment (linguistics)Control (management)Political scienceBusinessEconomicsEconomic systemMarket economyManagementPhilosophy

Abstract

fetched live from OpenAlex

This article offers a clear and approachable introduction to the evolving landscape of money and the frictions developing between traditional government control and decentralized finance (DeFi). Tailored for readers with a basic awareness of cryptocurrency but limited familiarity with its broader implications, the article demystifies DeFi by explaining its core concepts including blockchain, Centralized Bank Digital Currencies (CBDCs), and the historical role of government regulation of money through central banking. Against this backdrop, it examines the transformative potential of DeFi, emphasizing the growing tension between the centralized authority of governments and the decentralized ideals driving this new financial model. While governments seek to maintain stability and control, individuals increasingly gravitate toward the more affordable, efficient, and inclusive solutions promised by DeFi. Designed to empower readers with a better grasp of the forces shaping the future of finance, this article underscores the importance of understanding the delicate interplay between governmental oversight and decentralized innovation. As the digital economy expands, this dynamic struggle will influence not only economic policies but also personal financial choices and access to resources.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.190

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 studies0.0000.000
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 head0.014
GPT teacher head0.205
Teacher spread0.191 · 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 designObservational
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

Citations7
Published2025
Admission routes1
Has abstractyes

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