Mobile-Based Transportation Companies, Mandatory Arbitration, and the Americans with Disabilities Act
Bibliographic record
Abstract
Uber, Lyft, DoorDash and similar mobile-based transportation network companies (TNCs) have been involved in numerous legal battles in multiple jurisdictions. One contested issue concerns whether TNC drivers are employees or independent contractors. Uber recently lost this battle to some extent in the UK, but won it in California. Another issue concerns the TNCs’ use of mandatory (pre-dispute) arbitration clauses in their standard form service agreements with both drivers and passengers. These arbitration clauses purport to obligate such future plaintiffs to resolve any dispute with the defendant TNC outside of court and, typically, on an individual rather than a class basis. TNCs have had mixed success enforcing arbitration clauses contained in service agreements with their drivers under the Federal Arbitration Act (FAA). As for passengers, TNCs have been increasingly litigating disability-based discrimination claims brought against them and/or their drivers pursuant to the Americans with Disabilities Act (ADA). These claims have largely arisen in two situations.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.005 |
| 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".