Commentary: Buying Low, Donating High: Arrangements, Programs, Schemes and Scams
Bibliographic record
Abstract
This article discusses recent Canadian judicial treatment of “buy low, donate high” programs, where a taxpayer buys property at a fraction of its appraised fair market value (FMV) and donates it in order to claim a tax credit for an amount greater than the price paid for the property. The Supreme Court of Canada refused to hear appeals in Nash v. Canada and Klotz v. Canada. The Federal Court of Appeal ruled that for such “buy low, donate high” schemes, the FMV to be credited to the taxpayer is to be the amount paid for the property. The commentary concludes by outlining section 248(35) of the Income Tax Act. Created in 2003, the section deems that the FMV of donated property will be the lesser of the donor's cost and the actual FMV, depending on the timing and purpose of the acquisition and donation of the property. While the author condones addressing abuse of the tax system with “buy low, donate high” programs and suggests that the government should have denied all tax relief in the case of donation schemes, she is concerned that section 248(35) may be overly broad when applied in cases of legitimate donations.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.050 | 0.033 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".