Bibliographic record
Abstract
Abstract Tax avoidance and evasion is a hot topic. On the evasion (illegal activity by individuals) front, the various leaks culminating in the Panama Papers have once again revealed the scope of evasion by the global elite. Gabriel Zucman conservatively estimated the annual revenue loss at $200 billion. On the tax avoidance (legal activity by corporations) front, the OECD BEPS project has estimated the scope of avoidance by multinationals at between $100 and $240 billion per year. By comparison, total US corporate tax revenues are about $400 billion per year. The articles in this volume reflect various aspects of these troubling phenomena (from the perspective of citizens who pay their tax bills and bear the burden of budget cuts by governments starved of revenues). Yuri Biondi writes from an accounting perspective about the firm as an Enterprise Entity and the tax avoidance conundrum. Matthias Thiemann and Tim Buettner discuss the political economy of the OECD BEPS project. David Quentin discusses tax avoidance in transnational supply chains of multinationals. Blazej Kuzniacki analyzes tax avoidance in EU law, which has been the focus of a lot of activity recently (e. g., the proposed Anti-Tax Avoidance Directive by the EU Commission). Amir Pichhadze and myself discuss the idea of statutory General Anti-Abuse Rule (GAAR) in the US and Canadian contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".