The design of a corporate income tax system and how to protect it for the East African Federation
Bibliographic record
Abstract
The East African Federation (EA Federation) currently is an ideal of the East African Community (EAC) regional integration project. This study is premised on the basis that the EA Federation does come into existence. Moreover, this research is focused on the international hard law approaches to issues identified in this dissertation. This dissertation designs the most important features of a corporate income tax system for the EA Federation and proposes mechanisms by which to protect it. This study further designs a tax base inspired by the European Commission’s proposed common consolidated corporate tax base. Corporate tax rules derived from the provisions of the European Union’s Anti-Tax Avoidance Directive (ATAD) are included to protect the tax base. Further protections are provided through the design of a general anti-avoidance rule (GAAR) and a tax treaty policy. The proposed GAAR is constructed through the combination of elements from the existing GAARs in the EAC Partner States and the GAARs in the European Union’s ATAD, and the Income Tax Acts in Canada and South Africa. The proposed tax treaty policy is a derivation of international best practices, focusing on meeting the challenges developing countries face when concluding double taxation agreements. While this research may be premised on a fictional supranational organization, this study has present-day value in that it proposes how an African regional integration project may offer possible solutions on how to successfully integrate several corporate income tax bases into one coherent tax base, competently supported by mechanisms designed to protect it.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".