Cultivating Green Space Together: Exploring the Collaborative Planning and Public Engagement of Green Space in Edmonton, Alberta
Bibliographic record
Abstract
Edmonton, the fifth-largest municipality in Canada, is attracting more and more people to study, work, and live. Expected to nearly double in population by 2050, the City of Edmonton put forward a series of plans to enable a sustainable and climate-resilient city to accommodate new residents. Green spaces, including natural areas and urban open spaces (such as public parks and squares, corridors, linkages, and main streets), are increasingly regarded as dominant elements in promoting environmental sustainability and quality of life in cities. Therefore, how to plan and manage the green space while welcoming more people has become a key question for the City of Edmonton to consider. This research aims to critically explore the formulation of green space strategy from different perspectives under the collaborative planning and public engagement context. The objectives are to 1) elucidate the engagement process between the government and various stakeholders/public; 2) explore the engagement experience from different perspectives; 3) make recommendations for the collaborative green space planning as the plan is rolled out and acted upon. A social constructivist worldview adopted, and mixed qualitative research methods, including semi-structured interview, case study, and document review are applied to explore the topic. Each interview is approximately 40 to 60 minutes, and the potential interview participants may come from the municipal government, stakeholder organizations, or the local community. A case of the public engagement system in Edmonton was formed during this study. The research concludes by offering some salient lessons for those interested in advancing socially robust green space planning methods which integrate community engagement within planning and municipal government. Key findings suggest a shared view amongst city officials and community members as to the benefit of engagement. These variously were seen to include, for example, benefits to promoting public understandings of greenspace planning and municipal governance; values in connecting citizen experience of greenspace with ecological and sustainability planning; and supporting perceptions of belonging, ownership and responsibility for Edmonton’s green spaces. The research also identifies two key challenges to be addressed. The significantly included the difficulty of maintaining long-term relationships between City officials and wider public networks; challenges in protecting, or upholding, public voices within the bureaucratic policy process once an engagement or plan is complete. The thesis concludes by proposing the potential of simplifying planning and policy hierarchies as a means of addressing the above challenges, puts forward the value of moving from consultative to collaborative models of engagement and the need for publics to be better integrated within actual decision-making.
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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 | 0.002 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 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".