Bibliographic record
Abstract
This dissertation examines the discursive production of new developments and the reinvention of suburban image in the municipalities arcing around the City of Toronto, Ontario, Canada. Planning policies promoting densification, alongside rising housing prices, and persistent concerns about car-dependence, set the context for aspirations of urbanity in planning and development. Discourses analyzed include transcripts of interviews with housing and community producers (planners, municipal councillors, and developers), the planning policies produced by government, and the marketing materials produced by the development industry. Studies examining the historic planning and promotion of the suburbs have shown the prominence placed on symbolic distinction from the city with references to nature, rurality and countryside, where residents are promised a healthy retreat, privacy, safety, and an ideal place to raise children. This dissertation argues that suburban planning and housing marketing discourses in the Toronto region reveal an emerging reversal in the suburban script that downplays the urban-suburban distinction and promotes a more urbane place image, representing a case study of broader contemporary efforts to urbanize the suburbs. In the first of three empirical papers, the research shows how stacked and back-to-back townhouses are planned and promoted by intertwining discourses of suburban "evolution" and "routes to maturity" through homeownership where smaller units offer a type of fix to market constraints, and young adults are produced as suburban-re-inventor subjects. The second paper demonstrates the importance of aesthetics and modern design in the discursive production of competitive and attractive world-class growth nodes and corridors as part of the ideological-political trajectory towards post-suburbanization. The third paper examines the gap that producers describe between the promises of compact city theory and the in-practice realities of car-dependence and separated land uses. While some marketing materials draw on the long-standing discursive production of the suburbs as the "best of both worlds," producers describe the contemporary suburbs as increasingly compact in residential areas, but still car-dependent leading to concerns about being "squished-in" and stuck in traffic. Practitioner perspectives on the successes and challenges of current strategies signal the need for additional theories and policies, beyond residential densification, to resolve the challenges of the suburbs.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".