Chapter 35: Canada: Transfer Pricing Adjustment after a Competent Authority Agreement: Sifto Canada Corp. v. The Queen
Bibliographic record
Abstract
Why this book? This book is a unique publication that gives a global overview of international tax disputes on double tax conventions and thereby fills a gap in the area of tax treaty case law. It covers the 35 most important tax treaty cases that were decided around the world in 2017. The systematic structure of each chapter allows for the easy and efficient study and comparison of the various methods adopted for applying and interpreting tax treaties in different cases. With the continuously increasing importance of tax treaties, Tax Treaty Case Law around the Globe 2018 is a valuable reference tool for anyone interested in tax treaty case law. This book is of interest to tax practitioners, multinational businesses, policymakers, tax administrators, judges and academics. Downloads Sample excerpt, including table of contents This book is part of the Tax Treaty Case Law around the Globe Series View other titles in the series Editor(s) Eric C.C.M. Kemmeren, professor of international tax law and international taxation and Chairman of the Tax Economics Department of the Fiscal Institute Tilburg of Tilburg University, the Netherlands, and member of the board of the European Tax College. Peter Essers, professor of tax law and Chairman of the Tax Law Department of the Fiscal Institute Tilburg of Tilburg University, the Netherlands, and member of the board of the European Tax College. Daniël S. Smit, professor at the Fiscal Institute Tilburg of Tilburg University, the Netherlands. Cihat Öner, associate professor at the Fiscal Institute Tilburg of Tilburg University, the Netherlands. Michael Lang, professor and Head of the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Jeffrey Owens, professor and Head of the Global Tax Policy Center at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Pasquale Pistone, professor at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business), associate professor at the University of Salerno, Italy, and Academic Chairman of IBFD. Alexander Rust, professor at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Josef Schuch, professor at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Claus Staringer, professor at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Alfred Storck, professor at the Institute for Austrian and International Tax Law, WU (Vienna University of Economics and Business). Contributor(s) Ricardo García Antón, Paolo Arginelli, Philip Baker, Michael Beusch, Yariv Brauner, Graeme Cooper, Tsilly Dagan, David G. Duff, Craig Elliffe, Peter Essers, Søren Friis Hansen, Guilherme Galdino, Werner Haslehner, Roland Ismer, Eric C.C.M. Kemmeren, Guglielmo Maisto, Christoph Marchgraber, Adolfo Martín Jiménez, João Félix Pinto Nogueira, Cihat Öner, Lysandre Papadopoulos, Katerina Perrou, Cees Peters, Marilyne Sadowsky, Luís Eduardo Schoueri, D.P. Sengupta, Daniël S. Smit, Mirna Solange Screpante, A.J.A. Ton Stevens, Karolina Tetłak, Anne Van de Vijver.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".