Bibliographic record
Abstract
The choice between a bright line and a nuanced approach is one of the cornerstones of judicial law making. Yet the nature and implications of this choice remain to be fully understood. The term "bright line" is often used ambiguously to refer to two distinct line-drawing techniques. The first construal of the term refers to the variability in determining the circumstances that qualify the facts for the application of the law (the legal rule's antecedent), where the choice ranges between a bright-line rule, characterized by simplicity and unambiguity, and a multifold rule, which involves a complex, multifactor analysis. The second construal concerns the variability in the legal consequences assigned to these circumstances (the legal rule's consequent), where the choice is between a bright-line rule, leading to binary consequences, and a multifold rule, allowing for multiple possible consequences. Considerations about fairness and efficiency in the use of bright-line rules will be distinct, depending on whether the term is used in the first sense (a bright-line antecedent) or the second (a bright-line consequent). Variability in the antecedent affects the accuracy of the circumstances under which the rule applies: a bright-line antecedent provides greater simplicity at the cost of accuracy, whereas a multifold antecedent results in a more accurate determination at the potential expense of greater complexity. Variability in the consequent affects the granularity of the consequences of the rule: a bright-line consequent will have an all-or-nothing legal result, whereas a multifold consequent will have a more nuanced legal result. This article argues that the role of bright-line rules in encouraging tax-avoidance behaviour has been significantly neglected in the literature and case law. The poor understanding of how bright lines interact with the different components of legal rules has led to an underappreciation of the advantages and pitfalls of bright-line rules. This confusion has caused courts to mistakenly conflate this legal design choice with the distinction between legal form and economic substance. The article demonstrates the consequences of misinterpreting multifold rules, as shown by the Canadian courts' approach to defining "use" in interest expense deductibility, inadvertently facilitating prevalent tax-avoidance strategies.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.081 | 0.025 |
| Insufficient payload (model declined to judge) | 0.097 | 0.015 |
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".