Ontario's infrastructure boom: a socioecological fix for air pollution, congestion, jobs, and profits
Bibliographic record
Abstract
Infrastructure projects provide a spatial fix by increasing the scale and rate of capital accumulation, and because infrastructure projects themselves absorb massive amounts of productive and finance capital. I seek to explain the timing, character, and consequences of a Can $85 billion infrastructure boom in Canada's largest province, Ontario, between 2003 until 2013. I focus on the expansion and privatization of environmentally oriented infrastructure in Ontario via three policies: the conversion of public coal power plants to private natural gas and nuclear facilities; the Green Energy Act (a renewable energy feed-in tariffs programme); and The Big Move (a Can $50 billion dollar rapid transit plan using a public–private partnership model). I employ a Polanyian– O'Connor approach to emphasize the role of labour, environmental groups, and other social movement groups in bringing these investments about. I argue that infrastructure investments are not only a spatial fix aimed at finding capital a safe long-term investment and addressing class struggles around job creation. In Ontario these infrastructure investments have also provided the state with a broader socioecological fix for the economic and political contradictions stemming from air pollution and congestion. Social movements have pressured the government to address what James O'Connor refers to as the underproduction of the conditions of production: that is, degraded human health and quality of life; a deteriorating environment; and inadequate public infrastructure. Throughout the paper I emphasize how the socioecological fix in Ontario is being accomplished by advancing the neoliberal governance of public infrastructure. I point to how these neoliberal socioecological fixes are simply displacing socioecological crises to new spatial and temporal scales.
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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 | 0.000 |
| 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".