Bibliographic record
Abstract
This thesis presents a critical exploration of the ‘revitalization’ of Toronto’s Regent Park. Regent Park is Canada’s oldest and largest government subsidized housing development. Originally designed in 1947, Toronto City Council approved the revitalization of the neighbourhood in 2003. Within this thesis, Regent Park serves as a means to examine some of the ways in which urban planning and design, public policy, architecture and landscape architecture interact with people’s daily practices in their socioeconomic and cultural contexts, to ‘rebuild the social’. In order to do this, the thesis begins by presenting an account of the original development, providing a sociohistorical context for understanding the more recent revitalization. Secondly, the thesis provides a review of relevant theoretical literature pertaining to the idea that design shapes society, discussing key aspects of modernist and postmodernist accounts of the city, arguing for the salience of a broadly ‘relational’ model inspired by the work of Julier (2008) and others. Thirdly, the thesis conducts an empirical analysis of the recent revitalization process, using a mixed methodology of documentary analysis and in-depth interviews with a key developer and the residents of Regent’s park. This analysis explores the ideological commitments at play within the planning process, as well as the practice of planning itself, investigating how theories of design and planning relate to the actual process of planning, including the political and financial obligations. The analysis then compares the intentions of the design with the inhabitant’s lived experience within the space, focusing on the inhabitants’ active role in negotiating the space in ways that were ‘unplanned’. This thesis provides a sociological exploration of Regent Park as a complex site of interaction between the design of the space (influenced by theories of design, as well as economic, political and social motivations), the materials that make up that space, and the actual use of the space by residents, the outcomes of which challenge deterministic accounts of urban development.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.001 |
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".