The State of Uberisation: Neoliberalism, Smart Urbanism, and the Regulated Deregulation of Toronto's <scp>Taxi‐cum‐Ridehail</scp> Market
Bibliographic record
Abstract
Abstract In 2016, the City of Toronto legalised the ridehail giant Uber under a particularly Uber‐friendly regulatory regime. Rather than understanding this interim outcome along the lines of now widespread narratives of corporate “disruption”, in this article I take up Manuel B. Aalbers’ notion of “regulated deregulation” in order to foreground the state's role as a manically prolific facilitator of early Uberisation. Based on ethnographic research in Toronto, I argue that the three longer‐standing state spatial strategies of (1) the common‐sense neoliberal state, (2) the labour‐averse competition state, and (3) the tech‐infatuated smart state were paramount in creating those “on‐the‐ground” conditions—social, legal, spatial, and other—on which Uber has been able to thrive in many cities across the North American continent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.032 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".