Understanding public policy limits to the enforceability of forum selection clauses after <i>Douez v Facebook</i>
Why this work is in the frame
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Bibliographic record
Abstract
This article explores the nature of public policy limits to the enforcement of forum selection clauses, recently considered by the Supreme Court of Canada in Douez v Facebook. The public policy factors relied on by the plurality of the Court, inequality of bargaining power and the quasi-constitutional nature of the right at issue, possess neither the doctrinal clarity nor the transnational focus necessary to guide the deployment of public policy in this context. Here, I argue for a public policy exception to the enforcement of forum selection clauses based on the doctrine of mandatory overriding rules. This approach would focus on whether a forum selection clause has the effect of avoiding the application of local norms intended to enjoy mandatory application in the transnational context. This conception of public policy would be a more coherent guide to the exercise of courts’ discretion to enforce forum selection clauses in cases like Douez.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it