Bibliographic record
Abstract
This article examines the judicial interpretation of subsection 163(2) of the Income Tax Act, which penalizes taxpayers for false statements or omissions arising from "knowledge" or "gross negligence." The article specifically focuses on the Federal Court of Appeal's decision in Canada v. Paletta, arguing that courts have inappropriately broadened the scope of the wilful blindness doctrine while insufficiently addressing the aspect of gross negligence. The author advocates for a narrower application of wilful blindness, consistent with other areas of law, and an expanded interpretation of gross negligence. This approach, through the incorporation of a "cluster of ideas" framework, would provide greater certainty and fairness for taxpayers, tax practitioners, and the Canada Revenue Agency in determining culpability for false statements and omissions. The author proposes a novel analytical framework for the interpretation of subsection 163(2), considering factors such as risk and consequences, wilful intent, culpable inadvertence, and departure from a standard of care. This framework is designed to guide courts in assessing taxpayers' conduct with a more balanced approach that more closely aligns with Parliament's legislative intent. Finally, the author underscores the importance of maintaining a clear distinction between wilful blindness and gross negligence in tax law, suggesting that this clarity will enhance the competitiveness and predictability of Canada's tax system, and better guide taxpayers in managing tax-related risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".