Park Perceptions and Racialized Realities: Visualizing Social and Health Equity in Public Urban Greenspaces
Bibliographic record
Abstract
The growing literature indicates that natural environments, such as urban greenspaces, can promote health and wellbeing. However, the pathways are still unclear. The tendency to romanticize nature, without considering issues of equity and marginalization, presumes that everyone experiences greenspaces in the same ways, with universal positive impacts. Park Perceptions and Racialized Realities is a community-engaged and participatory photovoice study that critically examines the experiences of racialized people in public urban greenspaces in two underserved neighbourhoods in Toronto, Canada. This research took place during the unprecedented COVID-19 pandemic, a time when inequitable access to high-quality, safe urban greenspaces was amplified. Methods were adapted to take place online and grounded in feminist and anti-racist community-engaged principles. Participants attended online sessions, took photographs and videos on neighbourhood greenspace visits, and debriefed their experiences in individual interviews. First, a collaborative analysis process was facilitated with community residents and advisors. This process then informed a deeper thematic analysis of the photographs and narratives. Eight key themes are identified: (1) belonging and social connection, (2) exclusion, (3) mental health and wellbeing, (4) right to play and children’s recreation, (5) maintenance inequities, (6) access and accessibility, (7) safety, and (8) gentrification and complex use of public space. These findings are outlined in a community report, alongside policy and practice recommendations. Furthermore, public urban greenspaces influence three dimensions of wellbeing for racialized residents: (1) mental, (2) physical, and (3) social. These dimensions are unpacked in nine key domains to posit an aspirational framework. However, there are social and structural barriers that hinder these pathways to wellbeing. Residents also described issues of inequitable urban greenspace distribution and maintenance, lack of meaningful participation for racialized communities in greenspace planning and design, the lack of understanding of the diverse needs of racialized communities and the macro-level forces that create complex inter and intra-racial dynamics in greenspaces. This dissertation provides novel qualitative and visual insights into the experiences of racialized people to support public health professionals, landscape architects, planners, parks professionals and others in related fields to center equity and justice in public urban greenspace scholarship, policy, and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".