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Record W4396666248 · doi:10.59403/1sgf6qg014

Chapter 14: Canada

2012· book-chapter· en· W4396666248 on OpenAlexaboutno aff
Geoffrey Loomer

Bibliographic record

VenueEC and international tax law series. · 2012
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Why this book? Taxation of Intercompany Dividends under Tax Treaties and EU Law, comprising the proceedings and working documents of an annual seminar held in Milan on 1 October 2011, is a detailed and comprehensive study on the taxation of cross-border dividend distributions. It first considers cross-border dividend taxation in the context of EU law. In this field, issues such as the jurisprudence of the European Court of Justice, the hindrance to the internal market caused by double taxation of dividends and the compatibility of dividend withholding taxes are dealt with. Next, the book discusses the taxation of dividends under tax treaties, in particular focusing on the definition of “dividends” in the OECD Model Convention and the meaning of the concept of “beneficial owner” as applied to dividends. The application of domestic and agreement-based anti-abuse rules to dividends is thoroughly analysed. Finally, the relevance of the non-discrimination provision enshrined in Art. 24 of the OECD Model Convention to dividends as well as procedural issues relating to treaty relief and possible ways of improvement are taken into consideration. Individual country surveys provide an in-depth analysis of the above issues from a national viewpoint in selected European and non-European jurisdictions. This book is essential reading for all those dealing with cross-border taxation, EU tax law and tax treaty issues. Downloads Sample excerpt, including table of contents This book is part of the EC and International Tax Law Series View other titles in the series Editor(s) Guglielmo Maisto Contributors John F. Avery Jones, Philip Baker, Peter Blessing, Reinout de Boer, Frederik Boulogne, Maximilian Bowitz, Kim Bronselaer, Paolo de’Capitani di Vimercate, Emilio Cencerrado Millán, Katharina Daxkobler, Philippe Freund, Sebastian Heinrichs, Mark S. Hoose, Philip Kerfs, Katarina Köszeghy, Michael Lang, Koen Lenaerts, Geoffrey Loomer, Alexandre Maitrot de la Motte, Philippe Martin, Angelo Nikolakakis, Elisabeth Pamperl, Kees van Raad, Jacques Sasseville, Kelly Stricklin-Coutinho, C. John Taylor, Peter Wattel, Frans Vaninstendael

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 score0.962
Threshold uncertainty score0.988

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.018
GPT teacher head0.201
Teacher spread0.183 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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