Ride-hailing applications in Vancouver, Canada: Representation, local empowerment and resistance
Bibliographic record
Abstract
Technological applications have caused a revolution in the way individual transportation rides are offered and taken in cities all over the world. The adoption and regulation of ride-hailing has been the subject of heated discussion involving elected officials, bureaucrats, industry proponents, the traditional taxicab industry, and civil society. To implement ride-hailing, proponents and platform operators confront an intricate web of decision-making processes and institutional politics. In this way, existing normative processes shape the emergent regulation of such transportation network companies. This article analyzes the case of Vancouver, Canada, one of the largest cities in North America where ride-hailing companies belatedly secured authorization to operate from the provincial government in 2019. Focusing on the policy debate since 2012, the research identifies the interactions and processes of interest representation among various actors regarding this new transportation technology. The analysis shows how a variety of political, economic and regulatory strategies contributed to the delayed adoption.
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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 | 0.001 |
| 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".