Exploring Disparities in Park Access and Experience: A Case Study of Toronto, Ontario
Bibliographic record
Abstract
According to the City of Toronto Strategy (2019), Toronto has over 1,500 parks in approximately 7,700 hectares of land scattered throughout the City, equating to 28m2 of parkland per person. This paper explores the provision of parkland throughout the City of Toronto, while intersecting the practice of urban and environmental planning with wider themes of environmental justice and equity. If parks are unevenly distributed, then so are the benefits that they provide. This research paper looks beyond the geographic distribution of parks, to critically examine the quality and user experience of these public spaces in socio-economically contrasting neighbourhoods to attempt to highlight themes of environmental inequity and environmental injustice in the context of the City of Toronto. Through this essay, I will argue why the practice of urban planning and more specifically, parks planning in a neoliberal context such as Toronto, works to perpetuate injustices that already exist through the exclusion of participatory planning practices. I argue that it is vital to equitable parks planning to create meaningful community engagement opportunities that considers the varying needs of contrasting communities. This study will build on existing theoretical and empirical conversations on how the intersection of socioeconomic inequality, racialized poverty and environmental degradation disproportionately impact vulnerable groups in Toronto and how different levels of access to quality park spaces contribute to environmental justice. Through intense site observations, a created site audit tool, as well as questionnaire responses, this study uncovers the different qualities and user experiences that exist at parks within four neighbourhoods which consist of contrasting socio-economic characteristics. The results of this study demonstrate that user experience and park quality are much greater in the neighbourhoods of higher socioeconomic statuses or that have recently received investment through urban revitalization processes. Findings also highlight the importance of considering the unique needs of a particular neighbourhood and the residents, rather than a one-size-fits all approach when planning and enhancing local parks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".