Unproven Allegations of Dishonesty, Wilful Blindness, or Gross Negligence: The Case for Cost Consequences in the Tax Court of Canada
Bibliographic record
Abstract
The Income Tax Act and the Excise Tax Act impose penalties on taxpayers who fail to comply with their obligations “knowingly” or “under circumstances amounting to gross negligence” (“KGN penalties”). The case law has established a high threshold for “gross negligence,” holding that it requires the taxpayer to have shown “a high degree of negligence tantamount to intentional acting.” The minister of national revenue bears the onus to prove the facts supporting the imposition of a KGN penalty. Since 2018, in appeals of KGN penalties that proceed to judgment, the Tax Court of Canada has completely vacated the penalties approximately 30 percent of the time, on the basis that either there was no non-compliance by the taxpayer, or the taxpayer’s non-compliance was unintentional and did not result from a high degree of negligence tantamount to intentional acting. In the civil courts, as endorsed by the Supreme Court of Canada, adverse cost consequences can result when a party alleges fraud or dishonest conduct and fails to prove the allegations (“the fraud costs rule”). This article argues that the Tax Court should adopt the fraud costs rule and allow taxpayers to claim enhanced costs when the Crown fails to meet its burden of proof when litigating a KGN penalty. Allowing taxpayers to presumptively claim such costs would, it is hoped, motivate the minister to take greater care when deciding whether to assess and litigate KGN penalties, which would in turn help to avoid the unfortunate situation—of which many troubling examples exist—of KGN penalties being unreasonably and improperly proposed or imposed on taxpayers.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.012 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 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".