Resistance Along the Rails: Confronting Deindustrialization and Urban Renewal as a Neoliberal Socio-Ecological Fix through Social Movement Alliance-Forming in Toronto, Canada
Bibliographic record
Abstract
This dissertation analyzes post-Fordist social movement coalitions between labour, community and environmental groups that responded to, but also helped constitute, processes of deindustrialization and so-called urban renewal in Toronto, Canada between 2004-2015. I theorize this urban restructuring not only as a spatio-temporal fix but as a more encompassing “socio-ecological fix” that better accounts for the socio-ecological constraints and opportunities placed on the production of urban space for and by capital, labour and a wider range of social (movement) actors. The emergence, development, efficacy and impacts of two labour-community coalitions in Toronto are analyzed by integrating macro-scale theories of coalitions (i.e., eco-Marxist and Gramscian approaches) with social movement theory and ethnographic approaches. The Mount Dennis Weston Network (2007-2012) sought to create “green jobs” on a brownfield site before scaling-up into the Toronto Community Benefits Network (TCBN). The TCBN (2013-2015) aimed to win Ontario's first “community benefits agreement” (CBA) as a tool for leveraging rapid transit infrastructure investments to achieve other social and environmental policy objectives (e.g., employment equity, social procurement, environmental design, etc.). I appraise these coalitions as efforts to demand a right to the city—i.e., more democratic and egalitarian production of urban space (including the regulation of the urban metabolism)—and in terms of social movement alliance-forming that I define as a two-way shift in scale: an institutional broadening (or organizational scaling-up) and an ideological deepening (i.e. praxis). These social movement processes require more extensive on-the-ground organizing within neighbourhoods and unions without which coalitions risk becoming coopted and confined to negotiating minor concessions and trade-offs that deepen socio-ecological tensions and contradictions. In this way, discourses around “green jobs,” “sustainable cities,” and “community benefits” have been used by the state and corporations to organize consent for neoliberal urban governance (i.e., increased privatization and deregulation of the transit and energy sectors, and transit-led gentrification).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".