Bibliographic record
Abstract
Why this book? Tax treaty arbitration is a topic that has become more important than ever before. In a post-BEPS world, where tax treaty disputes are expected to increase significantly as a result of the different measures taken to address treaty abuse, a well-functioning dispute resolution mechanism is key to solving tax treaty disputes. Both the OECD and the European Union took initiatives to improve the mechanisms for dispute resolution in tax matters. The OECD amended article 25 of the OECD Model on the mutual agreement procedure and the European Union adopted Council Directive 2017/1852 on tax dispute resolution mechanisms in the European Union. The aim of this book is to provide tax authorities, policymakers, courts and practitioners an overview of the effectiveness of tax treaty arbitration and the approach towards the recent changes in 36 countries. This book comprises 36 national reports from countries across the globe and is the outcome of a conference on tax treaty arbitration that took place from 5 to 7 July 2018 in Rust (Austria). More than 100 experts, including the authors of the national reports, were brought together to discuss recent developments in the field of tax treaty arbitration. A general report highlights the most important findings of the conference. Downloads Sample excerpt, including table of contents This book is part of the WU Institute for Austrian and International Tax Law – Tax Law and Policy Series. View other titles in the series Editor(s) Michael Lang, Jeffrey Owens, Pasquale Pistone, Alexander Rust, Josef Schuch, Claus Staringer and Alfred Storck are professors at WU Vienna University of Economics and Business, Institute for Austrian and International Tax Law. Assistant Editor(s) Svitlana Buriak, Shimeng Lan, Alexandra Miladinovic and Jean-Philippe Van West are teaching and research associates at WU Vienna University of Economics and Business, Institute for Austrian and International Tax Law. Contributor(s) Kristiina Äimä, Ifeanyichukwu Azuka Aniyie, Muhammad Ashfaq Ahmed, Rahul Batheja, Alexander Bosman, Catherine Brown, Kirsten Burmester, Mateus Calicchio Barbosa, Fatima Chaouche, Nevia Čičin-Šain, Adrien Clinard, Lucas de Heer, Marilena Ene, Emmanuel Igwe Eze, Eivind Furuseth, Sriram Govind, Daniel Gutmann, Gordana Ilić-Popov, Petra Kamínková, Borbála Kolozs, Jiří Kostohryz, Katharina Kubik, Paolo Ludovici, Henri Lyyski, Aleksandra Maksimovska Stojkova, Michelle Markham, Željko Martinović, Yuri Matsubara, Clement Okello Migai, Peter Nias, Dana Olzhabayeva, Katerina Pantazatou, Céline Pasquier, Bart Peeters, Cristóbal Pérez Jarpa, Katerina Perrou, Pietro Piccone Ferrarotti, Pasquale Pistone, Rodrigo Polanco Lazo, Dejan Popović, Evgenii Pustovalov, Natalia Quiñones, Isabelle Richelle, David Rüll, Andrey Savitskiy, Luís Eduardo Schoueri, Fernando Serrano Antón, Madeleine Simonek, Ganda Christian Tobing, Laura Turcan, Danil V. Vinnitskiy, Adrian Wardzynski, Elizabeth Whitsitt, Felipe Yáñez Villanueva, Liao Yixin, Lidija Živković.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.445 | 0.194 |
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".