The Economies of Space Making a Case for Sustainable Urban Development
Bibliographic record
Abstract
Across disciplines, there has been a fascination with the concept of sustainability and it continues to pervade academic and professional discussions and discourses.In this research project, I study a real estate project in the hot property market of Toronto that is socially, environmental and economically sustainable.Based on this research, I came up with conclusions and strategies that could incentivize such developments.This research project is composed of three parts.In part one, I defined sustainability by looking at it through three lenses: academia (through a literature review), practice (through four industry standards that administer sustainability and finally policy (through summarizing the policy documents in Ontario).These definitions set the groundwork for part two of this project where I took the Alexandra Park revitalization project as a case study.Based on the definitions of part one, I evaluated the sustainability goals of the project and conducted a stakeholder dialogue with key informants from Tridel, Toronto Community Housing Corporation and Urban Strategies to determine the challenges, risk, incentives and prospects of sustainable development.The research of part one and part two were then synthesized in part three where four conclusions and five strategies are proposed to make sustainable development more feasible,
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.050 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".