Designing a General Anti-Avoidance Rule for the East African Community – A Comparative Analysis
Bibliographic record
Abstract
The East African Community (EAC) is a regional integration project working towards the formation of a political federation. As a grouping of developing states, most EAC Partner States have legislated their own general anti-avoidance rules (GAARs) as a means to prevent base erosion and profit shifting. This article argues that the EAC federation, once formed, should continue this practice and legislate its own GAAR to protect its corporate tax base – one of the most important tax bases for African countries. This article further proposes a GAAR for the EAC that builds on the existing GAARs in the EAC Partner States and draws from international best practice through a comparative analysis of the GAARs in the EU Anti-Tax Avoidance Directive, the Income Tax Act in Canada and the Income Tax Act in South Africa. In so doing, the author proposes a GAAR for the EAC that is in keeping with international developments while adapting such developments to the EAC context.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".