<scp>UBER IN EXURBIA</scp>: Peripheral Platformization, Post‐Suburbanization and the Public–Private Ridehail Partnership in the Toronto City Region
Bibliographic record
Abstract
Abstract After their widespread legalization, ridehailing companies Uber and Lyft soon embarked on a new stage of their respective business models: the initiation of a wave of strategic partnerships with local and regional transit agencies across the North American continent. This article accounts for this trend by putting forward the concept of the public–private ridehail partnership (PPRP). It aims to render visible the PPRP as a variously contradictory attempt to splice Uber and Lyft's platform‐based business models with the existing social and physical realities of North American post‐suburban space. While conceived as a strategic response to pressing sub‐ and exurban problems such as low physical densities, widespread car centrism and extensive transit undersupply, the PPRP, as I argue, is neither able to adequately address these dilemmas nor to ultimately resolve them. Rather, the PPRP latches onto old—and sets in motion new—powerful dynamics of heightened uneven development and continued urban entrepreneurialism. Each of these two dynamics is explored through empirical analyses of two recent PPRPs in the Toronto city region: the Lyft–Metrolinx pilot carried out between July and December 2019; and Uber's ongoing partnership with the town of Innisfil, located about 80 km north of downtown Toronto.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".