Private Car, Public Oversight: Municipal Regulation of Ride-hailing Platforms in Toronto and the Greater Golden Horseshoe
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Municipalities in many regions of Canada have regulated vehicle-for-hire services. With the rise of ride-hailing platforms, such as Uber and Lyft, this responsibility to produce a reliable vehicle-for-hire service has largely been transferred to private platforms. Using a case study of the City of Toronto and surrounding Greater Golden Horseshoe, this article examines how local regulation of this critical urban mobility service has changed. Drawing upon an analysis of 27 interviews with municipal staff, councilors and industry experts, a review of written local media, and a review of government documents, the study finds that municipalities are withdrawing from direct control of the industry due to a lack of tools of oversight and a prioritization of private industry over public service. The study discusses ongoing challenges that may be addressed by greater oversight of the service. It concludes by highlighting examples of municipalities growing their capacity for oversight and provides recommendations for further growth.
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.
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 | 0.000 |
| 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 it