Uniformity and Diversity in the Enforcement of Arbitration Clauses in Canada
Bibliographic record
Abstract
Arbitration is well established in Canada. All jurisdictions have implemented the 1958 New York Convention, the UNCITRAL Model Law on Arbitration and equivalent legislation for domestic arbitration. This generally supportive legal landscape for arbitration is often at odds with access to justice for consumers. As a result, several jurisdictions in Canada have adopted legislation to guarantee consumers’ access to local courts, including through class actions, notwithstanding the inclusion of arbitration clauses in their contracts. The constitutional division of powers in Canada entitles each province to adopt its own policy, leading to diversity across the country with regard to the enforceability of arbitration clauses in consumer contracts. In this paper, the author examines the tension between general support for arbitration and differentiated treatment of consumer arbitration in Canada. To that end, the author examines relevant legislation in several provinces (including Quebec and Ontario) as well as recent jurisprudence from the Supreme Court of Canada (Dell Computer (2007), Telus (2011) and Wellman (2019)). The 2020 decision from the Supreme Court of Canada in Uber may signal a new openness toward extending protection to other vulnerable contracting parties such as employees.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".