Governance matters: Regulating ride hailing platforms in Canada's largest city‐regions
Bibliographic record
Abstract
While scholars acknowledge the important role played by regulators in managing ride hailing and its impacts on cities, few studies have explored the implications for developing ride hailing regulations at different levels of governance. This paper examines regulatory approaches to ride hailing in Canada's three largest metropolitan regions: Toronto, Montreal, and Vancouver. Though united by a national border, these three regions are subject to unique governance approaches, histories, and trajectories. Although ground transportation in Canada usually falls within local or municipal authority, only Toronto, amongst these three city‐regions, treats the regulation of ground transportation as a local responsibility. In both Montreal and Vancouver, which are subject to different regulatory and governance structures, ride hailing is primarily regulated at the provincial level. As a more detailed examination reveals, we can further distinguish the three metropolitan areas based on the extent of intraregional coordination and regional mobility. By introducing these two concepts, the paper explores both the intended and unintended regional impacts of ride hailing regulations. These findings are relevant not only to ride hailing, but also to broader questions about governing both regional mobility and the platform economy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".