Bibliographic record
Abstract
Vast and thinly occupied, massive but not dense, the horizontal Canadian metropolis has been shaped by waves of economic, social and political forces. The expansive growth of the modern city, built on complex systems of industrialization, rapidly accelerating transport and communication networks and a culture of consumption, has left indelible marks on the urban landscape. By the end of the 20th century the thorough reduction in industrial capacity of North American cities made apparent the actual cost of this landscape in the form of derelict waste landscapes and underused monofunctional infrastructure in and around the traditional city centre. Partially in reaction to this dispersed field or carpet of development, the relatively young form of practice refered to as landscape urbanism has emerged as a lens through which we can better conceptualize and design for the complex social, economic and environmental contexts of the post-industrial city. In an effort to explore the principles of this emerging mode of practice and how they could provide alternate and more balanced methods of urban development, this Master's Degree Project (MDP) investigates the convergence of landscape urbanism, architecture and infrastructural systems and their ability to shape the 21st century Canadian city. Ultimately a proposal for the redevelopment of a large swatch of derelict industrial land and underused space along Victoria's waterfront is presented as a way to test the theoretical background through a specific design praxis.
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 | 0.000 | 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 | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.377 | 0.096 |
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".