Transportation network companies and the ridesourcing industry : a review of impacts and emerging regulatory frameworks for Uber
Bibliographic record
Abstract
New technological innovations in the passenger transportation industry in the form of “ridesourcing services” (colloquially known as “ridesharing”) are disrupting and transforming the taxi industry. The legality of these new ridesourcing services has been challenged by jurisdictions across the world. Governments have begun reform of existing vehicle for hire regulations, creating a new “transportation network companies” regulatory category for ridesourcing services. Commissioned by the CIty of Vancouver, this study has five research objectives: 1) review existing research and literature on ridesourcing; 2) review the impacts of ridesourcing, particularly on the taxi industry; 3) review the legislative and regulatory responses to ridesourcing; 4) identify potential regulatory frameworks for the City to consider; and 5) provide strategic recommendations and considerations for the City in developing a regulatory framework. The study provides a comprehensive foundation to inform future work on vehicle for hire regulations in the City of Vancouver.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".