Rethinking the Interpretation of Bilingual Legislation: The Demise of the Shared Meaning Rule
Bibliographic record
Abstract
This article assesses the value of the meaning as an approach to the interpretation of bilingual statutory provisions in which discrepancies occur between the two language versions. It identifies the three types of discrepancies that arise, shows how those discrepancies originate in drafting errors, and examines the ability of the shared meaning rule to uncover those errors. It then contrasts the rule's approach to the manner in which the courts have interpreted divergent text in similar situations. Based on this analysis, it suggests that the shared meaning rule is unsatisfactory from a theoretical perspective and that it lacks predictive value. The article proposes that in cases of linguistic divergence, rather than looking for a meaning shared between the two language versions, the courts should search for the single meaning that is most harmonious with the scheme of the Act and its apparent purpose. It concludes that courts should discard the shared meaning rule and instead start with a presumption favouring clarity and then interpret each version using the standard techniques of statutory interpretation-looking to the purpose of the Act, its internal consistency and legislative evolution, and the relevant presumptions of legislative intent-to determine which language version produces the most coherent legislative scheme.
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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 | 0.033 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".