Bibliographic record
Abstract
In Uber v. Heller – a case involving an employment class action subject to an international arbitration agreement – the Supreme Court of Canada decided three issues that threatened to undermine the enforceability of international arbitration agreements in Canada. The Court: (1) read the scope of the Canadian International Commercial Arbitration Acts narrowly; (2) created an exception to the competence-competence principle; and (3) relaxed the test for invalidating arbitration agreements on unconscionability grounds. At the same time, Uber was decided in a highly specific factual context and its ultimate impact on the enforcement of international arbitration agreements was largely left to be determined by lower courts in future cases. This article examines two such cases involving consumer class actions subject to international arbitration agreements. The article analyses the courts’ application of Uber and its effect on their reasoning and on the outcome of these cases. While it is difficult to predict how Uber will unfold in the lower courts over time, the two cases examined in this article suggest that Uber is unlikely to affect the enforcement of most international arbitration agreements in the context of consumer class actions – perhaps the context most akin to that of Uber – let alone in more traditional commercial contexts. Canada, international arbitration agreements, commercial, Uber v. Heller, employment, consumer, class action, enforcement
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".