Scale and jurisdiction in urban governance: Implications of the Uber regulations saga
Bibliographic record
Abstract
The proliferation of powerful ‘platform’ corporations such as Airbnb and Uber has exposed serious structural weaknesses in local urban regulatory systems. At nearly every angle, the local regulatory reach of local governments has proven too narrow to effectively corral large, multi-national platform firms. These firms are ultimately quite effective at sidestepping regulation, playing one jurisdiction against another and using their capital to lobby local politicians to sidestep public interest. The scale of local government places municipalities at an inherent disadvantage against these firms, amplifying the effectiveness of their commonly used tactics to gain a favourable regulatory environment. In this paper, we examine and compare these weaknesses of scale and jurisdiction, synthesize existing studies, incorporating both case studies and grey literature, and make policy-recommendations for local actors, including a proposal to scale up the regulatory authority for such platform firms to better manage the demands currently placed upon municipalities for regulatory approval.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".