The general anti-avoidance rule: has it changed the face of tax avoidance?
Bibliographic record
Abstract
Since time immemorial, there has been a (continual) duel between the taxing authorities and taxpayers. In Canada, this duel between Revenue Canada and taxpayers has become even more fierce over the past fifteen years. As a result, the Minister of Finance enacted the General Anti-Avoidance Rule ("GAAR") (section 245 of the Income Tax Act) which officially came into force on September 13, 1988. GAAR is intended to limit the boundaries of acceptable tax avoidance, but in a climate in which the ability to reduce taxes by use of tax avoidance schemes is a source of great pride, the far reaching scope and uncertain application of GAAR make it the most controversial section of the Act. These features also make GAAR the most promising tool by which to curtail tax avoidance. Accordingly, the purpose of this thesis is to assess whether or not GAAR has changed the face of tax avoidance, and if so, how? In addressing this issue, the thesis takes the reader through a step by step analysis of GAAR's genesis. It starts with a review of former subsection 245(1) and the judicial antiavoidance doctrines existing prior to GAAR. This is followed by an analysis of the policy turmoil, having fairness as its core issue, which led to the rule. The conclusion will include an assessment of the final product. This step by step analysis enables the reader to understand the shortcomings of the pre-GAAR methods of dealing with tax avoidance and, subsequently, GAAR's potential. However, as all judicial decisions relating to GAAR are pending, inference from the principles established in recent tax avoidance decisions will be the basis for assessing GAAR's potential to circumscribe effectively tax avoidance. Despite the lack of case law interpreting GAAR, this thesis suggests that current judicial trends reveal that GAAR's success is subject to the somewhat aleatoric judicial system of a society which encourages tax avoidance. It is submitted that, in the end, GAAR has not changed the face of tax avoidance; it has simply modified the way taxpayers may reduce their tax liability and limited the class of people who can avail themselves of the benefits of tax avoidance. Certainly, GAAR does not contribute to the government's espoused policy of fairness in taxation.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".