Tax Design and Administration in a Post-BEPS Era: A Study of Key Reform Measures in 18 Jurisdictions
Bibliographic record
Abstract
In 2015 the OECD released its roadmap to address Base Erosion and Profit Shifting. The global tax reform package, with 15 Actions, is designed to equip countries with the tools they need to ensure profits are taxed where economic activity occurs and value is added. This volume is a comprehensive stock-take of the BEPS implementation that looks beyond a mere checklist of action or non-action to explore the experiences of 18 different jurisdictions. It highlights the different approaches taken by capital importing and capital exporting regions, developed and developing countres, OECD and non-OECD members and well as G20 and non-G20 members. Expert authors from Australia, Canada, China, Hong Kong SAR, India, Indonesia, Japan, Korea, Malaysia, the Netherlands, New Zealand, Nigeria, Singapore, South Africa, Thailand, the United Kingdom, the United States, and Vietnam have contributed chapters to this volume. Each provides the 'must-know' answers to questions that all stakeholders in the tax system are asking in relation to the domestic implementaiton of the largest reform of international tax the world has seen in a century.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".