Homes, Kitchens, Cupboards, and Food Rooms: Shifting Foodscapes in Toronto’s Regent Park
Bibliographic record
Abstract
Toronto’s Regent Park, Canada’s first and largest subsidized housing complex, is currently being restructured through a $1.5 billion public-private partnership which is significantly altering the material and socioeconomic make-up of the area. The Regent Park Revitalization is framed within the media and by policymakers as overwhelmingly positive and as benefiting long-term, low-income residents by economically, spatially, and socially integrating the neighbourhood into the City of Toronto. This dissertation examines the impacts of mixed-income housing development on gendered experiences of shifting foodscapes and argues that food is an important lens for understanding how poverty is portrayed and experienced. By employing an archival analysis of the history of Cabbagetown and Regent Park, paired with a qualitative understanding of lone mothers’ everyday lived experiences gained through eighteen months of ethnographic fieldwork, this research argues that food and gender have been, and continue to be, central to understandings of poverty associated with the area. Foodscapes is a place-based and relational concept that articulates the connection between the objective physical landscape of food provision and the social, cultural, political, and subjective experiences of food. This dissertation explores the tension between integration and displacement and argues that foodscapes are key sites for unpacking and illustrating these dynamics. It argues that the positive narratives of integration and accessible consumption associated with the celebration of new foodscapes obscures the role that new foodscapes play in exacerbating and heightening inequality and disparity by displacing and delegitimizing food-based relationships and coping strategies low-income women rely on. This study problematizes underlying assumptions about mixed-income housing developments as a poverty alleviation strategy while simultaneously documenting the food procurement strategies and food-related advocacy, creativity, resourcefulness, agency, and institutions that lone mothers living through the Regent Park Revitalization employ to sustain social networks, provide food, and contest narratives about the Regent Park Revitalization.
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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 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".