From Renegade to Regulated: The Digital Platform Economy, Ride-hailing and the Case of Toronto
Bibliographic record
Abstract
Despite concerns and criticism, municipality upon municipality has approved regulations that enable ridehailing,a digital platform activity, to legally operate. This paper frames and scrutinizes three prominent tensionssurrounding the operation and expansion of ride-hailing using the City of Toronto as a case study. It finds thatwhereas scholarly debate emphasizes defi nitions, distinctions, legal arguments and inequality, municipal debatehas centered on ride-hailing’s brief yet controversial history, inputs to the regulation process, and connectionsbetween ride-hailing, municipal governance and innovation. This paper highlights the role of municipalities inestablishing policy directions for the 21st century city that address changing and challenging issues, the impactof which reach far beyond the digital platform economy. RésuméMalgré les préoccupations et les critiques, nombreuses sont les municipalités qui ont approuvé des règlementsqui permettent les applications de « ride-hailing », une plate-forme numérique, d’opérer légalement. Cet articleexamine les tensions entourant l’opération et l’expansion d’application de « ride-hailing » de la ville de Toronto.Nous constatons qu’alors que le débat des académiques insiste sur les définitions, les distinctions, les argumentsjuridiques et les inégalités issues de ces applications numériques, le débat pour les municipalités fut concentrésur la brève existence, quoique controversé, de l’expérience de ces applications, des apports au processus de larégulation, et les connexions entre ces applications numériques et la gouvernance municipale et l’innovation. Cetarticle met en évidence le rôle des municipalités dans l’établissement des orientations politiques pour la ville duXXIe siècle qui traitent de questions qui confrontent des enjeux exigeant en pleine évolution. L’impact de cesorientations politiques va bien au-delà de la plate-forme numérique de l’économie.
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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 | 0.001 | 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".