When Is a Contract Not a Contract?: Douez v Facebook Inc. and Boilerplate
Bibliographic record
Abstract
With Douez v. Facebook Inc., the Supreme Court of Canada has started to digest the implications of standard form contracts, or boilerplate, in the on-line consumer market. In this case, four of a panel of seven judges ruled that a forum selection clause in Facebook’s “terms of use” could not be enforced to stay a privacy class action brought against Facebook in British Columbia. These four judges applied several different doctrines, including unconscionability and public policy, but considered the same factors and got to the same results. The factors included the inequality of bargaining power between consumers and Facebook, Facebook’s on-line ubiquity, lack of an opportunity to negotiate terms, that forum selection clauses implicate the public good of adjudication, and the quasi-constitutional status of the privacy rights being litigated. While the scope of the decision may be limited by the last two factors, Douez represents an update of the court’s understanding of digital boilerplate in light of contemporary economic work. I suggest that an expansive reading of the court's decision provides a useful framework for evaluating boilerplate going forward, a contractual analysis that recognizes mass digital standard forms' public importance.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".