The Dominant Voices in Double Taxation Agreements: A Critical Analysis of the “Dividend” Article in the Agreement between Uganda and the Netherlands
Bibliographic record
Abstract
Abstract In a bid to attract foreign investment, Third World countries are increasingly concluding Double Taxation Treaties with capital-rich countries, based on either the UN model treaty convention or the OECD model. Using the example of the dividend Article in the Uganda-Netherlands treaty, the discussion in this article illustrates the increased use of tax treaties to shift income from developing to developed countries. By essentially reducing the tax rate on dividend income to nil, that treaty significantly erodes Uganda's tax base. Such agreements raise concern, especially when one takes into account the fact that investment decisions are often driven by factors external to tax, meaning that reduced tax rates do not guarantee increased investments. Worse still, because developing countries are net capital importers, the benefits accruing from such treaties are often one-sided. The paper calls for a rethinking and redrafting of the UN model convention to ensure that the taxing rights of Third World countries are strengthened. In addition, since the practical reality is that developed countries often base their agreements (even with developing countries) on the OECD model, there is a need to amend the latter model to take this reality into account.
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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 | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".