Bibliographic record
Abstract
This paper is the first in the Canadian legal literature to address “tax elections”, which bestow upon taxpayers the ability to choose among two or more available tax treatments for a single taxable event. I argue that policymakers should adopt a rebuttable presumption in favour of setting default treatments according to the preferences of a majority of eligible taxpayers, unless a “penalty default” structure can be shown to convey sufficiently valuable information to the government. To illustrate how such a presumption would work in practice, I apply it to two similar but inconsistently structured tax elections in the Income Tax Act relating to transfers of property to a spouse and to a corporation (subsections 73(1) and 85(1), respectively). I find that the design of subsection 73(1) is sound—its majoritarian default of tax-deferring “rollover” treatment avoids unnecessary transaction costs and squanders no information-forcing role. On the other hand, subsection 85(1) is counter-majoritarian, and the information disclosed jointly by taxpayers and corporations via the 85(1) election can be obtained at lower cost by requiring corporations to routinely report information about contributions of property. Mandatory reporting would also bolster the government’s anti-avoidance efforts. Thus, amending subsection 85(1) to reverse its default treatment would make an important corner of the income tax less costly and, at the same time, more equitable.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 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".