Exploring Eco-gentrification as a result of Urban Linear Park Developments: A Case Study of The Meadoway in Scarborough, Ontario
Bibliographic record
Abstract
The potential urban linear parks have, to improve the quality of life in urban areas is becoming increasingly recognized in Canada, particularly in the City of Toronto. From a planning perspective, urban linear parks provide a unique opportunity to promote sustainability, urban green space, community empowerment and active transportation. Urban green space development of vacant lands and brownfields can also have transformative effects on cities through the rejuvenation of underproductive areas. With the increased densification of cities, linear parks, therefore, are regarded with great value and have emerged as the predominant means of bringing together the built and natural environments. \n \nGiven the disproportionate distribution of urban green space and the greater burden of urban environments felt in lower-income and ethnically diverse neighbourhoods, investment into urban green space development in these areas is especially important (Astell-Burt et al., 2014). The increasing prevalence of eco- gentrification, however, is raising awareness of the perpetual inequalities associated with urban green spaces (Black & Richards, 2020). Specifically, a growing body of literature regarding eco-gentrification recognizes the failure to consider existing residents’ needs in the design and planning processes. In general, urban green space development also tends to struggle with promoting social equity and environmental justice principles and policies, resulting in an imbalance of economic development, environmental improvement, and social and cultural needs (Gould and Lewis, 2017). \n \nUsing a case study of The Meadoway (the Gatineau hydro corridor being converted into a new urban linear park) in Scarborough, Ontario this report will expand on existing academic literature on eco-gentrification and the equity-related challenges in urban green space. The finding from this study will offer critical insights into how equity issues were considered in the early planning stages of a prominent urban linear park in Canada, which will expand our understanding of urban linear parks and eco-gentrification. Identifying strategies for mitigating eco-gentrification may also enable other jurisdictions to prioritize environmental justice, access, and equity during the planning stages of these redevelopment strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".