Rethinking Toronto's Middle Landscape: Spaces of Planning, Contestation, and Negotiation
Bibliographic record
Abstract
This dissertation weaves together an examination of the concept and meanings of suburb and suburban, historical geographies of suburbs and suburbanization, and a detailed focus on Scarborough as a suburban space within Toronto in order to better understand postwar suburbanization and suburban change as it played out in a specific metropolitan context and locale. With Canada and the United States now thought to be suburban nations, critical suburban histories and studies of suburban problems are an important contribution to urbanistic discourse and human geographical scholarship. Though suburbanization is a global phenomenon and suburbs have a much longer history, the vast scale and explosive pace of suburban development after the Second World War has a powerful influence on how suburb and suburban are represented and understood. One powerful socio-spatial imaginary is evident in discourses on planning and politics in Toronto: the city-suburb or urban-suburban divide. An important contribution of this dissertation is to trace out how the city-suburban divide and meanings attached to city and suburb have been integral to the planning and politics that have shaped and continue to shape Scarborough and Toronto. The research employs an investigative approach influenced by Michel Foucaults critical and effective histories and Bent Flyvbjergs methodological guidelines for phronetic social science. To do this, the analysis provided draws principally from archival materials, newspapers, plans and policy documents, and interviews to reveal how socio-spatial landscapes were made and remade both in thought and practice. In this regard, Henri Lefebvres theoretical ruminations on the production of space are also important. Even where not made explicit, the making and remaking of the spaces discussed reveal the near constant work of the conceived to intervene in and reorder the lived. The dissertation concludes with a discussion of how we might ask new and different questions about past and current rounds of city-building, so that good and just places to live are made more possible.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.060 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".