Unmixing the mixed questions: a framework for distinguishing between questions of fact and questions of law in contractual interpretation
Bibliographic record
Abstract
In Sattva Capital Corp v Creston Moly Corp, the Supreme Court of Canada established that contractual interpretation generally involves questions of mixed fact and law subject to a standard of palpable and overriding error, unless an extricable error of law is identified. The Court confirmed and specified this holding in a number of subsequent decisions. The new approach to appellate deference has sparked criticism from various parties in the legal community. A tension has emerged between the Supreme Court shifting away from the historical common law approach to deference and the appellate courts’ attempts to restore it. This article examines the theoretical foundations of this new case law development and proposes a methodological framework for distinguishing between questions of law and question of fact in contractual interpretation. The ultimate goal is to provide guidance on the choice of the appropriate standard of appellate review in this area. First, it is argued that the recent case law development introduced by the Supreme Court lacks rigorous analytical foundations and fails to provide adequate guidance on choosing the appropriate degree of deference on appeal. Second, it is contended that a useful methodological approach for distinguishing between questions of fact and questions of law is 1) to identify the cognitive task performed by the judge when adjudicating the contended issue, and 2) to assess the relative advantage of adjudicating actors in performing that cognitive task. Cognitive task refers to the type of judicial reasoning, or inferential activity, the judge performs when deciding an issue.
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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 | 0.103 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.013 | 0.113 |
| Scholarly communication | 0.025 | 0.056 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".