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Record W2995265966 · doi:10.59403/36p2mtf

Taxation of Foreign-Source Income of Resident Individuals

2019· book· en· W2995265966 on OpenAlexaboutno aff

Bibliographic record

VenueDoctoral series. · 2019
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsGlobalizationEnforcementGoods and servicesInternational taxationPublic economicsIncome taxResidenceEconomicsBusinessPolitical scienceTax reformEconomyMarket economyLaw

Abstract

fetched live from OpenAlex

Why this book? The 21st century is characterized by unprecedented economic and technological globalization. An increasingly free cross-border flow of goods, services, capital and workforces has led to a greater integration of economies across the world. These developments have also posed some challenges to national tax systems. A seemingly simple rule most countries have adopted and maintained for decades that residents ought to pay taxes on their worldwide income now has to prove its feasibility in the face of these new realities. There is an open question as to how to administer this residence-based tax regime in a world in which states’ administrative capacities are highly restricted to their national borders while their residents increasingly trade, invest and provide services across borders. This book aims to introduce a new way of exploring an old but increasingly important topic in income taxation: the enforcement of taxes on the foreign-source income of resident individuals. Central to this discussion is the emerging “automatic exchange of information” (AEOI) system. The author explores the emerging AEOI Standard among governments as a potential mechanism to address the issues and attempts to provide much-needed historical research, conceptual clarification and theoretical support of AEOI. The author also analyses the need for a fair international legal framework for AEOI and discusses the particularities and challenges associated with establishing such a framework. Downloads Sample excerpt, including table of contents This book is part of the IBFD Doctoral Series View other titles in the series Author(s) After completing his doctoral (PhD) studies in tax law at McGill University, Vokhid Urinov joined the Faculty of Law at the University of New Brunswick (UNB) as Assistant Professor in July 2015. Currently, his teaching extends to many areas of Canadian tax and corporate law, particularly personal taxation, corporate taxation, international taxation, business organizations and corporate finance. Professor Urinov’s research interests lie in international tax compliance, more specifically, international tax regimes such as AEOI, base erosion and profit shifting and transfer pricing issues. His research has been published in numerous peer-reviewed journals.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.022
GPT teacher head0.220
Teacher spread0.198 · 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
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
Published2019
Admission routes1
Has abstractyes

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