Private Car, Public Oversight: Municipal Regulation of Ride-hailing Platforms in Toronto and the Greater Golden Horseshoe
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.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".