Bibliographic record
Abstract
After a series of Supreme Court decisions limiting the use of class arbitration and allowing defendants to contractually prohibit it, many expected that the end of this form of arbitration was imminent. Others argued that, given arbitrators’ wide discretion and the limited scope for judicial review, class arbitration might continue much as it had before. The empirical data developed in this Article show that neither side is completely correct. Class arbitration with the country’s largest provider, the American Arbitration Association (AAA), has not ended, but it has changed significantly. Arbitrators’ willingness to find that a contract gives them jurisdiction to allow class arbitration has decreased dramatically. AAA’s publicly available awards demonstrate that the class arbitration system was neither dismantled nor unaffected. Instead, the arbitrators’ approach to the change wrought by the Supreme Court resembles that of judges. Some businesses have updated their contracts to include class waivers, but many arbitrations have gone forward under contracts that are not so clear. Although they once routinely ruled that class arbitration was permitted in such instances, arbitrators have now split nearly 50-50 on whether ambiguous clauses permit class arbitration. The arbitrators take the law seriously, and its inconsistencies have resulted in the present muddle. Unlike judges, however, arbitrators cannot write their way out of trouble by creating a general default rule. Their authority is simultaneously too broad and not broad enough.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.034 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.017 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".