Bibliographic record
Abstract
This article attempts to update a Canadian classic - the realist account of statutory interpretation published by John Willis in the Canadian Bar Review in 1938. Willis' insights are compelling and they remain relevant today. However, by focusing on the rhetoric of statutory interpretation, by far its weakest point, Willis disregards the considerable work that goes on when statutory interpretation is well done. This article draws attention to that work. Part 2 looks at the kinds of analyses relied on by good interpreters to establish that elusive goal, the intention of the legislature. These include textual, purposive, scheme, policy and consequential analysis. Part 2 examines the difference between easy and hard cases, then focuses on the techniques used by interpreters to carry out the different kinds of analyses and how these relate to the formal rules. Part 3 looks at the range of arguments interpreters may construct based on their preliminary analysis. Not every argument in statutory interpretation is about the meaning of words. Interpreters also confront drafter's mistakes, gaps in the legislative scheme, overlap and conflict, and language that is over- or under-inclusive. The structure of these different kinds of arguments is set out and illustrated in Part 3.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.071 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".