Narratives of exclusion: A photovoice study towards racial equity and justice in public urban greenspaces
Bibliographic record
Abstract
• Photovoice can elucidate subjective, lived experiences in public greenspaces. • Deep inequities in greenspace design and planning exist at a neighbourhood scale. • Study insights highlight need for procedural, recognitional and restorative justice. • A Critical Race Theory lens in greenspace design would centre racialized realities. • Exclusionary decision-making and systemic oppressions perpetuate inequities. During the COVID-19 pandemic, public urban greenspaces were sought as places of respite. However, deep inequities surfaced regarding who had access to safe high-quality greenspaces. The Park Perceptions and Racialized Realities study explored the experiences of racialized people in public urban greenspaces in Toronto, Canada. This qualitative, community-based participatory action research took place in two neighbourhoods. Adapting photovoice methodology, participants were invited to (a) go on two individual greenspace visits, taking photographs in response to prompts on their experiences, and (b) participate in an online semi-structured interview to debrief their photographs and experiences. Eighteen racialized participants took over 200 photographs and videos, which were collaboratively thematically analysed by a community working group. This approach informed a deeper thematic analysis focused on racial justice and equity. Findings were mapped onto four environmental justice principles: distributional, procedural, recognitional, and restorative. This framework allowed for findings to contribute to environmental justice discourse on urban greenspaces, leverage Critical Race Theory, and offer action-oriented considerations for greenspace design and planning that center racialized experiences. Racialized residents enjoy using public urban greenspaces but face barriers, including unequal provision, limited access, maintenance inequities, exclusion from design and planning processes and unmet needs. Greenspace planning often neglects lived experiences and reinforces systemic inequities derived from racism, falling into the same traps and tensions that Critical Race Theory has identified in other disciplines such as colorblindness, interest convergence and structural determinism. A critical race lens provides a critical, justice-oriented framework for improving equity in greenspaces.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".