Just rides: Ride-hailing, the capabilities approach and the just city
Bibliographic record
Abstract
Vehicle-for-hire services are critical for the local economy and are frequently regulated by municipalities. With ridehailing platforms taking a dominant role in organizing the industry today, the treatment of workers has emerged as a subject for political debate about what municipalities can do to support local workers. This case study examines how justice for platform drivers should be considered at the municipal scale. It does so by examining the experience of drivers in the City of Toronto and surrounding Greater Golden Horseshoe. Drawing on Susan Fainstein’s just city theory and particularly her use of the capabilities approach to human development, the paper examines how diverse values are expressed in the capabilities of individual drivers. Results show that drivers are drawn to the flexibility of the industry but are empowered only by sacrificing other capabilities and ultimately suffer from multiple vulnerabilities such as poor pay and unfair discipline. At the same time, the paper finds a latent solidarity amongst drivers that seeks empowerment against arbitrary and unilateral judgements from platforms or government regulators. The paper concludes by considering a renewed orientation for local authorities to support drivers by enabling driver capabilities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".