Factors that limit the efficacy of general anti-avoidance rules in income tax legislation : lessons from South Africa, Australia, and Canada
Bibliographic record
Abstract
General anti-avoidance rules (GAARs) are rules in income tax legislation \nintended to curtail impermissible tax avoidance. GAARs have another \ncritical function, namely informing taxpayers of the limits of permissible tax \navoidance. A GAAR is therefore an important provision which must be \neffective. A study of the historical and current experience with GAARs in \nSouth Africa, Canada, and Australia, however, shows that the efficacy of \nGAARs is limited. The GAARs of the countries studied show some \nsimilarities but also some fundamental differences. In spite of these \ndifferences, certain common factors working against the efficacy of these \nGAARs can be identified. It is argued that these factors entail the inherent \nweakness of GAARs, controversial indicators of impermissible tax \navoidance, uncertainty, the role of the judiciary, taxpayer aggression, and \nthe limitations of the law as a weapon against impermissible tax avoidance. \nAdmittedly, some of these limiting factors are difficult to overcome. For \ninstance, a precise definition of impermissible tax avoidance has proved \nelusive and this status quo is likely to persist. Nevertheless, it is argued that \nthese factors need to be acknowledged and addressed in order to create more \neffective GAARs in future.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".