Urban Greenspace, Civil Society and Science: The Creation and Management of the Rouge Park, Ontario, Canada.
Bibliographic record
Abstract
Earth is becoming more urban. As the human population continues the current trend of migrating towards urbanized regions, the pressures to develop urban greenspaces will inevitably increase. Greenspaces play a critical role in urban livability for both human and non-human beings. This research examines the creation and management of the Rouge Park (Ontario, Canada), which is a large greenspace approximately 46 km2 located in the eastern portion of the Greater Toronto Area. The output of this research consists of three parts. The first provides an identification of the relevant actors, and a detailed chronology of the social and political events that led to the establishment of the Rouge Park. The second section explores the competing narratives of science, conservation, and development that were fundamental in shaping the protected area that we see today. The final section examines the governance and administration of the Rouge Park, and investigates the activities and involvement of civil society actors working in its day-to-day management. Using qualitative methods, I demonstrate that science and scientific expertise can be powerful tools of legitimization for civil society actors. In particular, I examine the benefits and pitfalls of placing ecologically-based rationalizations at the forefront of conservation policy deliberations. Furthermore, I show that despite shortcomings in the governance structure of the park, the current arrangement has provided civil society actors with increased opportunities to shape their community. My results show that a locally grounded nature conservation movement can serve as a powerful motivating force for citizens to enact long-term environmental planning initiatives.
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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 | 0.003 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".