Community Engagement in Urban Forestry: Exploring Barriers and Strategies for Inclusion of Recent Racialised Immigrants in Toronto’s Urban Forest
Bibliographic record
Abstract
The correlation of inequitable urban forest distribution with race and income is well-documented. Yet, despite the recognition of the underrepresentation of racialised communities, Canada’s urban forestry sector is slow to address access disparities to information and engagement opportunities, which hinders the development of a comprehensive understanding of factors perpetuating inequity and undermines efforts to foster inclusivity and diversity in urban green spaces. Though many North American cities are aware of increasing racial, cultural, and ethnic diversity, little research has been done to explore how racialised immigrant populations are engaged (or not) in urban forest planning and practice. The current thesis takes a qualitative case study approach to fill the literature gap on community engagement in urban forestry. Using Toronto as a case study, it explores factors influencing inclusion in this sector. In this manuscript-based thesis, Chapter Two focuses on urban forestry organisations and their partners using the lens of colourblindness, to highlight the systems and structures in place that lead to the token inclusion and inadvertent exclusions of racialised immigrant communities. Chapter Three focuses on group discussions with recent immigrants to show that culture, systemic issues, and the natural environment influence an immigrant’s engagement with the urban forest. The chapter highlights the critical need of information and communication networks to influence recent immigrant engagement in this context. Chapter Four investigates the motivations of organisations that engage low-income, racialised, and immigrant communities, including practitioners in the conservation, community service and grassroots sectors. Here, I show that organisations frame their practices based on their objectives and highlight the importance of collaborative partnerships among sectors, leveraging expertise from each other and facilitating holistic and inclusive community engagement programmes in marginalised and racialised communities. Combined, the three chapters underline the nuanced difference between engagement with or in the urban forest and the many paths to engagement. Overall, my work highlights the need to use interdisciplinary and intersectional approaches in urban forestry and recommends the use of race as an analytical category, not as a descriptor variable. As well, it demonstrates the need to unpack power differentials among and within urban forestry stakeholders versus those excluded from the process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".