GAAR in the Tax Court after Canada Trustco: A Practitioner's Guide
Bibliographic record
Abstract
This article considers the interpretation and application of the general anti-avoidance rule (GAAR) by the Tax Court of Canada since the Supreme Court of Canada first waded into the GAAR debate in Canada Trustco and Kaulius. More specifically, the authors have mined the post-Canada Trustco decisions of the Tax Court for emerging trends, uniformity in the judges' decisions, and guidance for tax practitioners in developing and implementing tax plans for their clients. One surprise in this series of decisions is the level of success enjoyed by taxpayers at the avoidance transaction stage of the GAAR test: Evans, Overs, MIL Investments, McMullen, and MacKay were all decided in the taxpayer's favour at that stage. These cases, along with Univar, show an inclination on the part of the Tax Court to establish a set of predictable standards for the interpretation of GAAR, in accordance with the Supreme Court's preference for certainty, consistency, and predictability in the application of the provision. However, four other decisions-Desmarais, Lipson, CECO, and Copthorne-represent, to differing degrees, a departure by the Tax Court from a narrow guideline approach. The authors conclude that additional standards of GAAR interpretation are emerging in the Tax Court, which, in their view, will assist tax practitioners in the post-Canada Trustco environment in navigating the often uncertain waters of complex tax planning.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".