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Record W7052844279

UK & KSA VATs: A Cutting-Edge Proposal – Mini-Blockchain and VATCoin

2020· article· en· W7052844279 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2020
Typearticle
Languageen
FieldEngineering
TopicLaser Design and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsInvoiceContext (archaeology)JurisdictionSupply chainDue dateBlockchain
DOInot available

Abstract

fetched live from OpenAlex

This paper develops, extends, and clarifies themes introduced in five prior papers dealing with blockchain, and VATCoin in the context of both (a) the new VATs in the Gulf Cooperation Council (GCC), and (b) the mature VATs in the EU. Five additional papers on VAT technology advances in Fiji, with blockchain and VATCoin applications to New Zealand’s approach to online sales platforms (the Netlix Tax) are similarly referenced and extended. The GCC VAT papers were exploratory. For the most part, they were composed before any GCC jurisdiction had implemented a VAT, and in three instances even before the GCC Framework Agreementwas officially published. Today, VATs have been adopted in three of the six GCC jurisdictions: Saudi Arabia, the UAE and Bahrain. A fourth jurisdiction, Oman, had been only a few months away from implementation, but now is delayed. As to the EU, the prior papers directly responded to the request for public comment on the Commission’s October 4, 2017 proposal for “far reaching reforms” in the EU VAT.All cutting-edge VAT compliance regimes depend on a comprehensive, naturally occurring or mandated digital invoice regime. Whether the goal is to blockchain •an entire VAT ecosystem (as in Fiji), or •a discrete market segment like taxi cabs (in Quebec), or •the marijuana supply chain (as is proposed for US States), or •the remote sales of services through online marketplaces (as is proposed for New Zealand’s Netflix Tax), or •cigarettes that are susceptible to smuggling (as was proposed, and partially adopted in parts of the GCC), or •whether the goal is to monitor the tax and financial flows on the other side of a transaction, •the domestic and cross-border payments of VAT (as has been proposed with VATCoin in both the GCC and the EU), everything starts with the adoption of the digital invoice. This paper will focus on two representative VAT jurisdictions within different economic communities –the United Kingdom (UK) in the EU and the Kingdom of Saudi Arabia (KSA) in the GCC. Both are moving toward the adoption of comprehensive digital invoices. Neither have mandated it (yet). It is certainly not anaturally occurring phenomenon in either country.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score0.916

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)0.0000.001

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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