Bibliographic record
Abstract
In new legislation effective for taxation years commencing on or after October 1, 2023, Canada will implement its version of the recommendations of the Organisation for Economic Co-operation and Development set out in its 2015 action 4 report, addressing base erosion through interest deductions and other financial payments. In this article, the authors explore the computational aspects of the proposed excessive interest and financing expenses limitation (EIFEL) rules. The rules will limit the deductibility of interest and other financing expenses incurred by corporations and trusts, and require an adjustment to certain income that those entities derive from partnerships and controlled foreign affiliates. In November 2023, the authors co-presented a workshop at the Canadian Tax Foundation's annual tax conference dealing with the computations required by the rules. This article extends that work in light of the implementing legislation tabled in Parliament within days of that workshop. The authors begin with some background on the consultation journey toward these rules. Then they explore the math underpinning the rules and the variables underpinning the math. The article shows how practitioners can use math to develop an understanding of the rules, by zeroing in on the key variables and interrelationships that are relevant to the limitations. The authors provide readers with a systematic way to approach computations across multiple entities and thus minimize the work effort involved in applying the EIFEL legislation and navigating the transitional rules.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
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".