Bibliographic record
Abstract
This article reviews the recent landmark transfer-pricing case law in Canada. It suggests that the Canadian courts may have given birth to a sixth comparability factor for the application of the arm's-length principle that contravenes the intent of section 247 of the Income Tax Act. This new comparability factor is referred to as the relevance of non-arm's-length factors surrounding the relationship between related parties.First, the article briefly summarizes the process known as the comparability analysis, as set out in the guidelines issued by the Organisation for Economic Co-operation and Development and in section 247 of the Act. Second, the meaning given by the Canadian courts to the comparability analysis is examined. The author highlights how the courts, through their decisions, have created a sixth comparability factor for transfer-pricing purposes. The article concludes with a brief discussion of the unintended consequences that this new comparability factor may have for what constitutes 'reasonable efforts' to use arm's-length allocations or prices, and thus for the possible application of transfer-pricing penalties.
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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".