What was the Lump Sum Payment Intended to Replace in Tsiaprailis
Bibliographic record
Abstract
In February 2005, the Supreme Court of Canada dismissed the appeal in Tsiaprailis v Canada. The Court affirmed that 6(1)(f) of the Income Tax Act applies to the arrears portion of lump sum settlements in disability insurance claims. The Federal Court of Appeal found that the portion of Tsiaprailis' settlement lump sum representing arrears was caught by paragraph 6(1)(f) of the Act despite that it was not “payable periodically” as required by the provision. The Supreme Court applied the surrogatum principle: amounts received in lieu of amounts that would otherwise be taxable are themselves taxable. The majority found that the arrears portion of the settlement was paid to replace past disability payments. The payments would have been taxable as “pursuant to” the insurance plan per para. 6(1)(f) had they been received when due. The author argues that in Tsiaprailis, the whole settlement, regardless of the basis for its calculation, was paid to extinguish the individual's right to any benefits under the policy. The whole amount – both past benefits and future benefits – was thus a payment on account of capital and should not have been taxable per para. 6(1)(f) of the Act. As the law stands after Tsiaprailis, an amount that has been paid on account of accumulated arrears, whether a settlement or court ordered, is taxable per 6(1)(f) of the Act; payment for the release of the insurer's liability and the claimant's future benefits per the policy escapes taxation.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".