Bibliographic record
Abstract
Why this book? Taxation of Intellectual Property under Domestic Law, EU Law and Tax Treaties, comprising the proceedings and working documents of an annual seminar held in Milan in November 2017, is a detailed and comprehensive study on the taxation of intellectual property (IP). It begins with a comparative analysis of the domestic private law aspects of IP and the domestic tax regimes applicable to profits deriving from the utilization of IP. It next examines the taxation of IP under EU law, with a particular emphasis on (i) the EU fundamental freedoms and State aid, and (ii) the open issues in the implementation of the EU Interest and Royalty Directive. The book then moves to selected tax treaty issues. In particular, it analyses (i) the historical background and the policy of article 12 of the OECD Model Convention; (ii) the meaning of “royalties” and overlapping between articles 7, 12 and 13 of the OECD Model Convention; (iii) royalties in the context of the OECD Multilateral Instrument under the limitation on benefits (LOB) provision and the principal purpose test (PPT) clause; and (iv) certain selected issues on cross-border transfers of IP. Individual country surveys provide an in-depth analysis of the domestic tax regimes and actual tax treaty application and practices by various states, including Australia, Austria, Brazil, Canada, China (People’s Rep.), France, Germany, Italy, the Netherlands, Spain, Switzerland, the United Kingdom and the United States. This book presents a unique and detailed insight into the taxation of intellectual property in an international context and is therefore an essential reference source for international tax students, practitioners and academics. 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 Contributor(s) Paolo Arginelli, Celeste M. Black, Alberto Brazzalotto, Patricia A. Brown, Sophie Chatel, Robin Damberger, Robert J. Danon, Mathieu Daudé, David N. de Ruig, Sjoerd Douma, Anne Fairpo, Elizabeth Gil García, Hans-Peter Gradwohl, Peter Hongler, Michael D. Knobler, Na Li, Sean P. McElroy, Adolfo Martín Jiménez, Leonardo F. de Moraes e Castro, Larissa B. Neumann, Jacques Sasseville, Joel Scheuerman, Livia Schlegel, Florian Schmid, Julia Ushakova-Stein, Matthias Valta, Zoya Zalmai
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.155 |
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".