The production of nature in planning for urban expansion: A cultural landscape study of new urban growth in Oakville, Ontario
Bibliographic record
Abstract
The city's edge is a cultural landscape of conflicting and competing meanings. The visible, physical edge of rapidly urbanizing cities is emotionally charged, representing prosperity to some, and sprawl and environmental destruction to others. My dissertation is a cultural landscape study of city expansion at the edge of the Toronto-centred region, where urban growth pressures are as intense as anywhere in North America or Europe. My research reveals that ideas about the countryside are produced against the city, and these ideas are discussed in terms of ecology and natural heritage. This is a study of the cultural politics of the meaning of landscape in a contemporary planning process where local area planning comes face-to-face with the global environmental imagination. In the Town of Oakville, a wealthy suburb in the Toronto metropolitan area, a planning process to urbanize the last remaining countryside of the town has been underway for the past two decades. In the end, the decision to urbanize has been in lock-step with the decision to conserve: through the creation of a large natural heritage system (almost 900 hectares or more than 2,000 acres), fully one-third of the planning area, development of the remainder of the lands can take place. While pastoral ideas of the romantic countryside underlie the valuation of this landscape, representations of ecological sensitivity by environmental science were politically the most successful. Local area politics have undergone a revolution resulting from the negotiation over the future of this countryside. Using discourse analysis (text analysis of public planning process documents and popular media), participant observation of public meetings, and interviews with informants, my research reveals that cultural attitudes toward growth and conservation are informed by symbolic landscapes of country and city and these are implicated in the production of real landscapes and places. As planning practitioners and academics involved in the political process of shaping landscape change at the city's edge, it is difficult to represent those opinions of the public and other participants in the planning process that are not supported by scientific, empirical study. The lens of cultural landscape provides tools to understand and recognize cultural value, meaning and symbolism in edge landscapes and to engage with them in areas which are being planned for change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".