Stopping the ‘War on the Car’: Neoliberalism, Fordism, and the Politics of Automobility in Toronto
Bibliographic record
Abstract
This article interrogates the politics of automobility in Toronto under the regime of mayor Rob Ford, who came to power in 2010 promising to ‘stop the war on the car.’ The election of Ford, and the thrust of his subsequent agenda, came as a surprise to many in the city, due to Toronto’s reputation as a cosmopolitan diverse transit-friendly global city. The Toronto case study allows for the analysis of the relationships between Fordism, automobility, and the politics and rationalities of neoliberalism. Instead of seeing neoliberalism as something external or imposed, its contested politics are rooted in diverging social and economic interests directly derived from Fordism and the system of automobility, with opposing political-economic factions both drawing on different elements of neoliberalism. Authoritarian populist neoliberal regimes like the Ford administration in Toronto, and the roll-back austerity they promote, are not antithetical to automobile Fordism, but on the contrary represent an attempt to protect and reinvigorate it in the face of the forces of de-industrialization and financialization. As such they receive their support from social groups irrevocably invested in the continuation, and irrationalities, of the Fordist system of automobility. This has implications for how the politics of neoliberalism might unfold in the future.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".