Using Arbitration to Eliminate Consumer Class Actions: Efficient Business Practice or Unconscionable Abuse?
Bibliographic record
Abstract
Companies are increasingly drafting arbitration clauses worded to prevent consumers from bringing class actions against them in either litigation or arbitration. If one looks at the form contracts she receives regarding her credit card, cellular phone, land phone, insurance policies, mortgage, and so forth, most likely, the majority of those contracts include arbitration clauses, and many of those include prohibitions on class actions. Companies are seeking to use these clauses to shield themselves from class action liability, either in court or in arbitration. This article argues that while the unconscionability doctrine offers some protections, case-by-case adjudication is a costly means of attacking class action prohibitions. Thus, this article proposes that the interests of both public policy and efficiency would be better served by federal legislation prohibiting companies from precluding consumer class actions. This article has been cited by the Canadian Supreme Court in Seidel v. TELUS Communications Inc., 2011 SCC 15 at para. 166 (Mar. 18, 2011) (LeBel & Deschamps, JJ., dissenting).
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 | 0.038 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".