Social Actor Engagement in Municipal Decision-Making for Parks, Planning, and Civil Society in Edmonton, Alberta, Canada 1960-2010: Institutional Intersections
Bibliographic record
Abstract
Edmonton, Alberta, has a unique approach to public spaces that sees conjoined creation and development sharing of public spaces for the collective benefit of the community and stakeholders; this approach began 100 years ago. Green or open spaces, natural areas, the river valley, City of Edmonton and community recreation facilities, and public education facilities share common sites in a system of public spaces, hereafter called a “park system.” Rather than narrowly focus on government entities and technical processes this dissertation posits that parks decision-making occurs more broadly through dialogue and perspectives of social actors engaging with one another and how they impact each other’s perspectives, positions and decisions over time. This dissertation identifies and examines the interplay and relationships between the land use planning processes and community engaged park development, programming and maintenance activities and processes in Edmonton, using Strategic Relational Institutionalist and Historical Institutionalist perspectives. Categories of social actors involved in both institutions include elected officials, senior and frontline administrators, community nongovernmental organizations, school boards, community residents, developers, landowners, and consultants. Each of these actors has different levels of power and agency within administrative processes and within institutions themselves. This dissertation is an exploration of how land use and parks decision-making occurred over time. The Greenview and Blue Quill park spaces were identified in area plans -- park systems were approved in the early 1970s based on pre-existing legislation, strategic plans, policies, legal agreements, park master plans, and practices. Those previously approved documents effectively preconfigured the park systems and the specific site, configuration, location, and program in the land use planning institution. The land use planning institution turned farm land, wet lands, and treed areas into urban landscapes (i.e., residential, commercial, institutional, roadways, etc.), including generous allocations for park sites and land for schools. Once the sites were acquired, the community, administrators, and elected officials jointly constructed, programmed and maintained park lands through a series of construction funding agreements that built on and enhanced historical shared programming approaches. This work was also facilitated by policy initiatives that identified the community and community nongovernmental organizations as both partners in development and integral to local community decision-making of all kinds. Often termed animation of park lands, the first wave of co-produced development occurred by the end of the 1980s. Since that time, the parks were in continuous public use, until 2006 and 2009 when each site was reduced in size to accommodate housing. My tenure as a parks planner began in 1985 and continued until 2014. I was functionally engaged in the parks institution for about half of the case study period and engaged with mentors whose tenure went back another fifteen years. The opportunity to combine a practitioner lens with theoretical constructs to analyze events provides a number of contributions to theory and practice. This dissertation describes the planning process and institutional decision-making over an extended period of time specific to a park and park system (institutions and civil society); the system context had not been previously studied. The analyses identifies land-use planning social actors’ and park institutional social actors’ intersections (i.e., institutional planes) that recognize different social actors and different temporal considerations. The mobilizing characteristic of legislation, policies, and funding agreements to privilege and shape institutions is also illuminated. This dissertation provides nuance discussion of park discoursal practices, understandings, and realities that has not previously been explored. Finally, this dissertation reveals the unique characteristic of park lands infrastructure funded through substantial contributions by community social actors. Park infrastructure should be seen as a category of municipal infrastructure facilitated by legislation and municipal funding practices that is unique unlike any other form of municipal infrastructure. This unique characterization of park lands should lead to a broader discussion of equity and fairness in planning processes related to park lands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".