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Record W1956040498

How Might Cell Phone Money Change the Financial System

2010· article· en· W1956040498 on OpenAlexaff
Shann Turnbull

Bibliographic record

VenueJournal of financial transformation · 2010
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsFiat moneyCurrencyCommerceMedium of exchangeBusinessDemand depositStore of valueMonetary economicsFinancial transactionEconomicsLegal tenderMoney creationInvestment (military)FinanceDatabase transactionMonetary policyCentral bank
DOInot available

Abstract

fetched live from OpenAlex

The emergence of cloud banking in developing economies from billions of cell phones transacting both legal tender and informal units of accounts has created a need to reconsider habits of thinking about the nature of money and banking in advanced societies. The dysfunctional nature of modern money and banking is revealed by considering cell phone units of account based on four historical forms of money: (i) the current form of synthetic or “fiat” legal tender that can earn interest, (ii) fiat money that does not earn interest but has a usage fee, (iii) “free-money” issued privately with a usage fee, and (iv) “natural” money redeemable into specified goods and/or services with a usage fee. The value of a “green” form of natural money, redeemable into units of renewable electricity, becomes fixed by the investment cost of generators to create an inflation resistant unit of account. This paper identifies green dollars as offering a competitive medium of exchange for the “invisible hands” of (i) investors, (ii) Islamic economies and businesses, (iii) green voters, (iv) governments seeking to reduce the need for carbon taxing or trading, and (v) those seeking a reserve currency in case the financial system fails.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0070.011
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0130.004

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 head0.010
GPT teacher head0.198
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations11
Published2010
Admission routes1
Has abstractyes

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