Misguided Inferences? The Use of <i>Expressio Unius</i> to Interpret Tax Law
Bibliographic record
Abstract
This article explores how the interpretive canon of expressio unius has been used by the courts when interpreting the Income Tax Act, and discusses the canon’s place within the landscape of statutory interpretation of income tax law. The article reviews the existing literature to describe the canon, the assumptions on which the canon relies, and the reasons in favour of and against the canon’s use. The ultimate conclusion is there is some value in the interpretive tool, but it should be used only to prompt interpreters to ask questions instead of prompting them to draw conclusions. While canons of interpretation are generally considered textualist in nature, expressio unius type reasoning is often used as a way of taking into account the context of a particular provision. Another problem apparent in the case law is that the canon, also called implied exclusion, is often confused with the canon of implied exception. The article also examines court decisions that apply or reject the use of expressio unius when interpreting the Income Tax Act. Finally, the article proposes factors that should be considered when determining whether expressio unius should be used in a particular tax case.
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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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".