Bibliographic record
Abstract
In a context of rapid urbanization and increasingly standardized built environments, urbanism must find new methods of creating appropriate conditions for the variability of contemporary urban life. The city, understood as a system of interconnected processes in constant change, offers a relational way of thinking about urban design. This thesis explores the concept of Relational Urbanism through a strategic design approach that engages the complexity of the site to create variability in the built environment by relating built form to landscape elements. This relational approach has particular potential in post-industrial sites, where challenging existing conditions and processes of remediation resist conventional methods of redevelopment. The thesis focuses on the Toronto Port Lands as a testing ground for this design approach, drawing on the site's industrial heritage to develop a landscape framework and a set of relational rules that will guide the emergence of a diverse urban environment able to change over time. A series of design strategies—remediation parks, urban delta, adapted industry, and differentiated fabric—rethink the challenges of the site as opportunities for public benefit, creating a variegated landscape for built form to respond to. In contrast to a singular static master plan, this method favours multiple flexible strategies that can be deployed incrementally, breaking down the scale of development and allowing it to be realized by a wide variety of stakeholders. Through this approach the thesis seeks to enable the city to intentionally but subtly guide its urban landscape toward diversity and allow its citizens to participate in its continued adaptation.
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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.053 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".