Corporate Tax Planning: What Is a Tax Planner To Do After Deans Knight?
Bibliographic record
Abstract
The authors of this article review the current jurisprudential landscape surrounding tax-avoidance cases, in both the general anti-avoidance rule (GAAR) and non-GAAR contexts, for the purpose of assisting tax planners. The authors begin by addressing the challenges of achieving perfect certainty in the interpretation of tax provisions other than GAAR. They discuss how the background of judges can affect the decision-making process. They then review recent non-GAAR jurisprudence, highlighting instances where courts reject unacceptable tax plans and underscoring the courts’ generally unsympathetic stance toward aggressive tax plans that appear to defy common sense. The authors then analyze the Supreme Court of Canada’s decisions in Canada v. Alta Energy Luxembourg SARL and Deans Knight Income Corp. v. Canada. They compare the two decisions in detail and suggest how taxpayers might respond to these seemingly divergent judgments. Turning to the recently proposed amendments to GAAR, the authors question the necessity of these changes, in light of the Supreme Court’s recent guidance on the application of the provision in Deans Knight. The article concludes by offering practical suggestions for tax planners with the aim of equipping them to navigate GAAR in implementing tax plans.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".