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Record W2887273249 · doi:10.5539/ijef.v10n9p14

A Framework for the Development of a National Crypto-Currency

2018· article· en· W2887273249 on OpenAlexvenueno aff
Adam Abdullah, Rizal Mohd Nor

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing powerCurrencyStore of valueEconomicsPrice of stabilityScope (computer science)Monetary economicsMonetary policySettlement (finance)Monetary baseValue (mathematics)PaymentConceptual frameworkBusinessMacroeconomicsFinanceComputer science

Abstract

fetched live from OpenAlex

This paper seeks to provide a conceptual framework as to whether a central bank or a monetary authority should issue a crypto-currency given available technology and what are the consequences of doing so. Under the fiat standard the value and purchasing power of money has experienced an exponential decay, whilst prices have increased exponentially. Typically, a central bank is responsible for monetary and financial stability, including settlement and payment mechanisms. A proposed methodology is provided that involves both quantitative and qualitative analysis to measure the comparative monetary performance, stress testing and impact assessment of a new crypto-currency that includes backing by gold, silver and a basket of commodities. The scope of the proposed framework involves a monetary economic analysis, supported by a technological investigation, under the framework of Shari’ah compliance, to explore an impact assessment of the adoption of a national crypto-currency. The significance of this study is that, it provides a framework for the development of a new national crypto-currency, which retains its’ store of value in terms of monetary performance and price stability, that would also investigate whether it’s implementation is viable.

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: Methods · Consensus signal: none
Teacher disagreement score0.556
Threshold uncertainty score0.122

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 science0.0010.000
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 head0.024
GPT teacher head0.281
Teacher spread0.257 · 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
GenreMethods

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
Published2018
Admission routes1
Has abstractyes

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