“The (CISG) Road Less Travelled”: Case Comment on Grecon Dimter Inc. v. J.R. Normand Inc.
Bibliographic record
Abstract
At first glance, the Supreme Court of Canada's recent decision in GreCon Dimter Inc. v. J.R. Normand Inc. appears to be a case upholding the primacy of international commercial arbitration, choice of forum and choice of law clauses. Upon closer scrutiny, however, the Supreme Court of Canada failed to consider the application of the UN Convention on Contracts for the International Sale of Goods (CISG) to the overall dispute. Interestingly, the same choice of forum and choice of law clauses were considered by the United States Court of Appeals a year earlier in GreCon Dimter, Incorporated v. Horner Flooring Company, Incorporated. In either of the Canadian and American GreCon decisions, the parties' (and their respective counsel's) characterization of the legal issues, including jurisdictional arguments, ultimately guided the domestic forum court's jurisprudential analysis. Unlike GreCon v. Horner, choice of forum remained a live issue when it reached the Supreme Court of Canada in GreCon v. Normand. In both cases, the parties' choice of law remained an important, but not exclusive, factor in the domestic court's overall determination of proper forum. While the Supreme Court of Canada did not address the applicability of the CISG in GreCon v. Normand, perhaps another opportunity awaits Canada's highest court to contribute to the CISG's global jurisconsultorium.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.038 | 0.026 |
| Insufficient payload (model declined to judge) | 0.006 | 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".