Bibliographic record
Abstract
The use of debt is, according to the OECD, one of the simplest profit-shifting techniques available in international tax planning, due to the mobility of money. A debt tax planning strategy can be applied without any people or machinery being moved or re-allocated; it can be done by advisors or in-house specialist sitting at their desks. These (lawful) tax planning strategies undermine the fairness and integrity of tax systems, because multinational entities can use Base Erosion and Profit Shifting (BEPS) strategies to gain a competitive advantage over domestic entities, as well as undermining voluntary compliance by all taxpayers, according to the OECD. The focus in the OECD BEPS project and the Anti-Tax Avoidance Directive (ATAD) is to counteract these tax planning strategies to ensure taxation where the value is created (economic activity). This article focuses on one anti-tax avoidance measure: interest limitation rules as addressed in BEPS action 4 and in ATAD Article 4. The aim of the article is to address the need for interest limitation rules, analyse the recommend approached in BEPS action 4, analyse the minimum interest limitation rule in the EU according to Article 4 in ATAD, and the implications of OECD action 4 and ATAD Article 4. Lastly, the article will discuss alternatives to these interest limitation rules as a way to address BEPS.
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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".