Pathways towards equity: Solutions-focused workshops with urban greening professionals
Bibliographic record
Abstract
Urban green governance refers to the complex network of actors working across sectors to manage urban vegetation. While recent research has identified barriers to addressing green space inequities, there has been much less focus on how these barriers interact as self-reinforcing systems or on potential solutions. This study explores how urban greening professionals understand and operationalize pathways toward more equitable governance, applying a multidimensional justice framework that considers distributional, procedural, and recognitional aspects of equity. We conducted participatory workshops with 28 urban greening professionals in two Canadian metro regions, using a Future Wheel methodology that had participants collaboratively analyze barriers to equity and strategies for addressing them across seven locally relevant greening initiatives. Participants’ equity-related concerns included documented experiences of recognitional injustice, ambiguity regarding implementation processes, and competing priorities between actors. These concerns were reinforced by interconnected barriers: limited resources, insufficient coordination, and systemic constraints from existing governance structures. To overcome these challenges, participants recommended policy changes that prioritize multi-level collaboration and community engagement throughout planning processes. Participants believed addressing these interconnected barriers would have mostly positive implications—including improved governance capacity, more meaningful community engagement, and more equitable access to green spaces—though some acknowledged potential negative outcomes such as displacement or political backlash. Our findings highlight how barriers to equity persist through mutually reinforcing feedback loops, demonstrating the importance of reflexive, collaborative governance approaches that can address multiple dimensions of equity simultaneously while remaining adaptable to local contexts.
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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 | 0.023 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".