Tax secrecy and tax transparency : the relevance of confidentiality in tax law
Bibliographic record
Abstract
Contents: Eleonor Kristoffersson/Pasquale Pistone: General Report - Diego N. Fraga/Axel A. Verstraeten: Argentina - Kathrin Bain: Australia - Marion Stiastny: Austria - Denis van Bortel/Robert Neyt: Belgium - Luis Eduardo Schoueri/Mateus Calicchio Barbosa: Brazil - Allison Christians: Canada - Felipe Yanez V.: Chile - Linghui Ren: China - Esperanza Buitrago Diaz: Colombia - Lukas Moravec/ Danuse Nerudova: Czech Republic - Erki Uustalu: Estonia - Kristiina Aima/Kenneth Hellsten: Finland - Thomas Dubut: France - Matthias Valta: Germany - Katerina Pantazatou: Greece - Tamas Feher: Hungary - Yinon Tzubery: Israel - Sunil Gupta: India - Francesca Vitale: Italy - Matthias Langer/Martin Moosbrugger: Liechtenstein - Lionel Noguera/Anne Selbert/Katharina Muller: Luxembourg - Manuel Tron/Elias Adam Bitar: Mexico - Arjo van Eijsden/Kristy Jonas/Sanne Verhage: Netherlands - Shelley Griffiths: New Zealand - Klaus Bieberach Schriebl: Panama - Hanna Filipczyk: Poland - Antonio Carlos dos Santos/Clotilde Celorico Palma: Portugal - Basarab Gogoneata: Romania - Danil V. Vinnitskiy: Russia - Gordana Ilic-Popov/Svetislav V. Kostic: Serbia - Lidija Hauptman/Sabina Taskar Beloglavec: Slovenia - Hyejung Byun: South Korea - Fernando Serrano Anton: Spain - Eleonor Kristoffersson/Annina H. Persson/Joakim Nergelius/Filippo Valguarnera/Anna-Maria Hambre/Ylva Larsson: Sweden - Michael Beusch: Switzerland - Emrah Ferhatoglu: Turkey - Joshua D. Blank: USA.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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.
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".