Tax Design and Administration in a Post - BEPS Era: A Study of Key Reform Measures in 16 Countries
Bibliographic record
Abstract
The OECD’s Base erosion and Profit Shifting (BEPS) initiative is undergoing what may be the most challenging phase, namely ratification and implementation by countries and jurisdictions. In this paper we provide a preliminary overview of the approaches being taken in 18 jurisdictions, namely: Australia, Canada, China, Hong Kong, India, Indonesia, Japan, Korea, Malaysia, the Netherlands, New Zealand, Nigeria, Singapore, South Africa, Thailand, the United Kingdom, the United States and Vietnam. When the larger project is complete in early 2019, it will enable the global accounting profession to be apprised of the effect of the enhanced tax reporting and compliance requirements under the G20/OECD BEPS program). The paper provides some background on each of the jurisdictions, prior to reviewing their position on the Multilateral Convention to Implement Tax Treaty Related Measures to Prevent BEPS (MLI), the BEPS Inclusive Framework and the adoption of the four minimum standards of Actions 5, 6, 13 and 14. The paper then reviews the responses to the remaining BEPS Action items, as well as outlining unilateral measures across these jurisdictions.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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 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".