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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".