Analysis of misuse and abuse in terms of the South African general anti-avoidance rule : lessons from Canada
Bibliographic record
Abstract
In terms of the South African general anti-avoidance rule, a transaction that \nmisuses or abuses the provisions of the Income Tax Act may be disregarded \nfor tax purposes. The misuse or abuse provision, along with the general antiavoidance \nrule (GAAR), has not yet been judicially considered. It is argued \nthat the provision brings further uncertainty and breadth to the general antiavoidance \nrule. It calls for a purposive interpretation of tax legislation. This \napproach, however, creates uncertainty regarding the determination of \npurpose. In Canada, from which the provision was borrowed, the courts \ninitially applied a policy approach in determining purpose but this \ndisadvantaged the revenue authorities in a series of cases. The Minister of \nNational Revenue was required to present a clear and unambiguous policy \nwhich in reality could not be found. The thrust of this article is to show that \nthe misuse or abuse concept could turn out to be a lateral development in the \nSouth African GAAR because of the uncertainty it carries and if lessons on \nits application are not learned from the Canadian experience.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".