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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".