One Person’s Trash is Another Person’s Treasure: The Public Place-making of “Mount Trashmore”
Bibliographic record
Abstract
Across North America, brownfields serve as poignant symbols of neglect, environmental injustice, and community failure. More often than not, communities are left to suffer alongside these neglected landscapes. As a result, brownfield sites—often abandoned, contaminated, underutilized, and derelict—create a unique dilemma for urban and regional planners, city park administrators, and communities. Often recognized as valuable spaces, communities are beginning to demand that brownfield areas undergo remediation, revitalization, and redevelopment efforts to create a useable community space. As a result, all levels of government are committing resources to address this problem and facilitate revitalization efforts. In the context of brownfield redevelopment, there remains a strong case for park development given the assortment of social benefits generally associated with urban greening projects. Furthermore, brownfield redevelopment is generally well-suited to park development because it can revitalize natural habitats, limit urban sprawl and the consumption of agricultural land, and achieve environmental justice and sustainable development objectives (Moore, 2000). The current case study examines one instance of a proposed brownfield redevelopment project in the City of Kitchener, Ontario. Results of this study indicate the public reaction to the proposed redevelopment of a local brownfield site surprised city planners in that “Mount Trashmore” already had a community-based sense of place embraced by many residents. From the perspectives of policymakers, planners, and residents alike, plans for brownfield redevelopment must necessarily take into account the sociocultural meanings various stakeholders assign to the properties in question. In the present case study, two key concepts—sense of place and the privatization of public space—were identified as important contributors for support or opposition of brownfield redevelopment. Although residents surrounding the brownfield site believed redevelopment of the area was necessary, the proposed private park development threatened local residents’ access to the park as well as the sense of place attributed to the area. As such, managers must recognize the way their agencies move forward with the (re)development of landscapes, particularly brownfields, is not self-evident. The case of “Mount Trashmore” reveals the unique set of circumstances that characterizes brownfield redevelopment and makes the sense of place of local residents particularly relevant to explore.?
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".