Public Policy is an Unruly Horse and the Law of Contract is an Ass: A Comment on Douez v Facebook, Inc
Bibliographic record
Abstract
Online boilerplate contracts pose fundamental challenges to the traditional principles of contract law. Can a contract characterized by the complete absence of bargaining, choice, and the possibility of amendment be meaningfully characterized as a contract? Do consumers have a real choice as to the non-negotiable terms and conditions (including litigation avoidance clauses) presented by powerful digital platform firms like Google, Twitter, and Facebook? How far should the courts go in regulating these boilerplate arrangements, particularly in the abiding absence of legislative direction or reform? In Douez v Facebook, Inc, the Supreme Court of Canada considered for the first time the enforceability of a forum selection clause in an online boilerplate consumer contract. The Court’s answers—rendered in three sets of reasons—illustrate the tension between not only legal doctrine and public policy, but also between the courts and legislatures as sites of public norm generation and legitimation. The Court’s reasoning in Facebook continues a recent trend in its jurisprudence of blurring the lines between the application of doctrine and public policymaking. The result, quite apart from the equities or merits of the Court’s decision, furnishes further proof that public policy is not only an unruly horse, but that it is also capable of making an ass out of the law of contract.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.059 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 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".