Bibliographic record
Abstract
Recently, the Supreme Court of Canada in Uber Technologies Inc v Heller used the unconscionability doctrine to strike down a pre-dispute arbitration clause in an Uber driver agreement that required arbitration in the Netherlands. This has led some to ask: How would a court in the United States analyze this case? This comment will address this question, giving due weight to the US Supreme Court’s trend toward strengthening the Federal Arbitration Act (FAA) and enforcing pre-dispute arbitration agreements in employment and consumer contexts. Nonetheless, this comment diverges from critiques of unconscionability’s flexibility and lack of clear definition—which allegedly threaten efficiency in contract law. Instead, the comment urges that unconscionability remains steadfast in US law to protect core human values. Unconscionability is not a frivolous gloss on classical contract law. Instead, it provides a flexible safety net for catching contractual unfairness. Accordingly, one could argue that under US law, a court would find the arbitration clause in Heller unconscionable. However, a US court may have provided a different remedy.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.026 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".