The Regulation of Transportation Network Companies in the United States
Bibliographic record
Abstract
The “Uber phenomena” is the perfect case for a comparative law study: a global phenomenon, impacting almost every country, causing massive disruption of local monopolies and raising many of the same legal questions worldwide. In most countries, taxi companies have long enjoyed monopolies amidst strictly regulated markets. Uber’s emergence, combined with the development of a new economic model based on innovative technological tools, has profoundly impacted the industry. It all happened very fast. Uber launched operations in 2009 in the United States and, within a few years, has changed the face of the taxi industry throughout the world. The purpose of this book is to explain how various legal systems reacted and adapted to the disruption caused by the emergence of this new economic model. It is a book about economic regulation, it focuses on how the law regulates—more or less strictly—the actions of economic entities. This book is the collective work of an amazing teams of authors—all academics in law or lawyers—from around the world and representing twenty-two countries, namely Argentina, Australia, Belgium, Brazil, Canada, China, Colombia, Denmark, Finland, France, Germany, Greece, Italy, Japan, Lithuania, Poland, Portugal, the Netherlands, Spain, Switzerland, the United Kingdom and the United States.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".