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Record W2093762043 · doi:10.1504/ijbge.2010.029553

Curbing economic crime with RFID enabled currency

2009· article· en· W2093762043 on OpenAlexaff
Lorne D. Booker, Nick Bontis

Bibliographic record

VenueInternational Journal of Business Governance and Ethics · 2009
Typearticle
Languageen
FieldComputer Science
TopicBlockchain Technology Applications and Security
Canadian institutionsMcMaster University
Fundersnot available
KeywordsICTSLaw enforcementCurrencyBusinessEnforcementDigital currencyElectronic moneyEconomic crimeMoney launderingScale (ratio)Financial transactionCommerceComputer securityInformation and Communications TechnologyFinanceEconomicsMonetary economicsDatabase transactionLawComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Information and Communication Technologies (ICTs) enable us to conduct business efficiently and effectively on a global scale. At the same time ICTs provide criminals with new capabilities with which to circumvent law enforcement efforts. Consequently, law enforcement agencies need new tools and new capabilities. RFID enabled money (r-money) is one such tool. R-money would make money visible to information systems. In this paper the potential benefits of r-money are presented and some of the societal, technical and governmental issues associated with r-money are discussed.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.323

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.274
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
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

Citations1
Published2009
Admission routes1
Has abstractyes

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