From ride hailing to food hailing: Understanding on-demand food delivery through platform urbanism and urban policy in Canadian cities
Bibliographic record
Abstract
This paper examines the pivot that took place during the COVID-19 pandemic from ride hailing to food hailing. In 2020, the global spread of COVID-19 challenged urban life, raising questions about the prospects for digital mobility platforms. Pandemic restrictions resulted in dramatic changes in mobility patterns globally, including significant declines in demand for ride hailing. Concurrently, with restaurants closed to indoor and outdoor dining for extended periods in many cities around the world, restaurateurs worked diligently to adjust their business models. During the early days of the COVID-19 lockdown, ride hailing firms such as Uber shifted their efforts from moving people to moving food and other goods. With a particular focus on Canada, we document this pivot and analyze its significance using evidence from case studies and interviews with multiple actors involved in the food and platform delivery ecosystem in major North American cities. We discovered both mutually beneficial and friction-filled relationships in the business, social and organizational logistics of digital food delivery. These results have implications for theories of platform urbanism and urban policy including highlighting new forms of competition that prioritize the role of urban infrastructure for creating value for platform firms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| 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".