Beneficial Ownership Provisions In Tax Treaties Between Developed And Developing Countries: The Canada/South Africa Example
Bibliographic record
Abstract
In the years since the Organisation for Economic Cooperation and Development (OECD) adopted its first draft tax treaty in 1963, the world has experienced an astonishing surge in international trade and investment. The tax treatment of these cross-border transactions is affected by double tax agreements. As tax treaty networks will likely continue to expand, concerns about tax treaty abuse might be expected to grow. The extent to which a countrys tax treaty policy favours developing countries - or not - depends upon the extent to which the country is prepared to adopt provisions from the UN model tax convention as opposed to the OECD model. Developing countries, in particular, should carefully consider the design of their tax treaties so as to effectively combat tax avoidance without sacrificing foreign direct investment. To this end, the Canada/South Africa tax treaty is compared and contrasted with these two models. The concept of beneficial ownership is reviewed in this context. It is contended that a general definition in South Africa's Income Tax Act of 'beneficial ownership' would assist in the interpretation of the term for the purposes of South Africa's tax treaties. It is submitted that the scope for the source taxation of passive investment income (viz. dividends, interest and royalties) in the developing country could be magnified through treaty negotiations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".