Bibliographic record
Abstract
How should a sham be treated for tax purposes? In 1524994 Ontario Ltd. v. M.N.R., the Federal Court of Appeal treated a sham as if it reflected the true agreement between the parties in order to uphold a GST assessment. The result was inconsistent with existing jurisprudence and undesirable. Courts should apply the law to the true facts only, and should not overlook or give effect to a sham in order to achieve the desired juridical consequences. The author reviews the origins and development of the sham doctrine and introduces a three-part typology of sham cases. In situations like 1524994 Ontario Ltd. v. M.N.R., the sham is intended to obtain non-tax benefits from a third-party victim, but in the process triggers unintended tax consequences, which are the subject of litigation. Although the traditional approach in Continental Bank Leasing Corp. v. M.N.R. (under which recharacterization is permissible only if the label attached to a transaction does not reflect its actual legal effect) could result in non-payment of taxes and retention of improperly obtained benefits, the author concludes that this result would be preferable to that of the Federal Court of Appeal judgment. Treating a sham as real, and taxing a wrong as a right (1) will not deter parties from creating shams to obtain non-tax benefits, (2) will violate longstanding principles that tax law be applied with neutrality and equity and without considering its effects, and (3) will increase uncertainty and inconsistency in the case law.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.076 |
| Scholarly communication | 0.013 | 0.026 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.016 |
| 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".