Healthy Environment, Healthy Economy, Healthy Humans: Planning for Toronto's Waterfront Revitalization Throughout the 20th and 21st Century
Bibliographic record
Abstract
This paper focuses on the revitalization process of the Toronto Port Lands. The paper moves through the historical transitions of the Toronto Port Lands, looking at three eras of plans, which have helped to shape the Toronto Waterfront. The first plan begins with the industrial city between 1912-1954, which had planned the waterfront based on what was best for the economy and industries at the time. This time period left the Toronto waterfront to answer several pressing questions for decades to come. The plan looked at questions of jurisdiction and what to do with an unusable brownfield site after the destruction of land in a declining industrial era. This set out David Harvey’s theoretical approach to the intersection of capitalism and space within an urban context. Throughout the three plans we are able to see how society is dominated by the tensions between politics, capitalism and planning. The second timeframe looked at David Crombie’s Royal Commission on the Future of the Waterfront’s 1992 comprehensive ecosystem approach, which focused on the revitalization of the environment and restoring the damage caused through the industrial period. This would in turn create a thriving economy yet widen social divisions within the city. The third timeframe analyzed is a contemporary neoliberal based planning approach focused on; the environment, economy and humans, which is where the question of specializing in everything is truly brought to the forefront of the Toronto Port Lands 2010 plan. The idea of specializing in everything although a utopian thought for planners, reinforces the assumption that the freer the market, the greater the social inequalities. The paper then examines global cities, specifically; Vancouver, Sydney and San Francisco, which all have their own niche specializations throughout their waterfront planning processes that have helped shape their city, yet contribute to their own urban imbalances. This paper argues that planning is informed by dominant ideological planning perspectives of the particular period of time. In the case of planning for Toronto’s waterfront we can see how the paradoxical goal of wanting to specialize in everything creates tensions in planning and further contributes to social-division, as the underlying goal of each of the plans examined relies on the political realities of the time and the pursuit of capital accumulation within the urban context.
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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 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".