Neighbourhood Engagement in Urban Forest Governance: A Case Study of Resident Associations in Mississauga, Ontario, Canada
Bibliographic record
Abstract
Initiatives in community engagement and the struggle to conserve urban forests have risen steadily over the past 30 years. In Canada, Resident Associations (RAs) are one major group, with varying degrees of influence, that have shown potential to build working relationships with decision-makers at the local level. Although poorly studied, RAs commonly work with local or municipal representatives and engage civic managers in order to address issues at the neighbourhood scale. Urban forest and tree health have become of increasing concern for local communities because of the greater awareness of the physical and social benefits these green spaces provide to society. Unfortunately, there is little understanding as to the role of public participation in urban forest governance despite it being a key component in building frameworks for successful urban forest governance. The present study examines the role of RAs in influencing governance at the local level using the City of Mississauga, Ontario, Canada as a case study. In-depth, semi-structured interviews and a grounded theory approach reveal increased engagement by RA executive members when the benefits of and risks to their neighbourhood urban forest are understood. Increased urban forest knowledge allowed RA executive members to become involved with municipal decision-makers and more likely to participate in developing strategies and networks to conserve and improve urban forest health. Interview questions probing the power dynamics between residents and decision-makers indicated most residents build upon common objectives within existing civic processes rather than work independently from the outside for conservation. Research here demonstrates the key role that knowledge plays in both motivating and sustaining resident involvement in urban forest governance, and provides clear evidence that RAs need to build strong relationships with decision-makers and neighbourhood constituents in order to effect change. Resident knowledge about the urban forest helps raise social capital and build strategies for RA engagement in order to achieve better management.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".