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Record W4317893922 · doi:10.4324/9780429327292

Mobile Payments, Consumer Policy, and the Law

2023· book· en· W4317893922 on OpenAlexaboutno aff
Nwanneka Victoria Ezechukwu

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentBusinessMobile paymentInternet privacyLaw and economicsEconomicsComputer scienceFinance

Abstract

fetched live from OpenAlex

Mobile technology offers an innovative and cost-effective channel for delivering a range of financial services, including mobile payments. In some jurisdictions, mobile payments simply provide a convenient option for facilitating payment transactions. In other jurisdictions, mobile payments are viewed as potentially transformative because they present an opportunity to expand access to financial services. However, as with other innovations, mobile payments raise consumer protection concerns and require robust regulatory mechanisms to address such concerns. Against this backdrop, the book adopts a typology of consumer policy tools which can be used to address the identified consumer concerns. This typology guides the enquiry into the existing consumer protection frameworks applying to mobile payments in selected jurisdictions (Canada, Kenya, and the United Kingdom). The main objective of this endeavour is to identify best practices that national authorities seeking to leverage mobile payments and similar innovations can emulate. This book will be of interest to policymakers, regulators, industry stakeholders, students, and scholars interested in the regulation of innovative financial services, particularly from a consumer protection perspective.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.633
Threshold uncertainty score0.997

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.020
GPT teacher head0.224
Teacher spread0.204 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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