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Record W6959070034 · doi:10.7939/r3-ayfb-ce71

Cultivating Green Space Together: Exploring the Collaborative Planning and Public Engagement of Green Space in Edmonton, Alberta

2020· dissertation· en· W6959070034 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2020
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholder engagementSustainabilityGovernment (linguistics)Public engagementStakeholderSpace (punctuation)Community engagementPlan (archaeology)Public participation

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0300.012
Scholarly communication0.0080.002
Open science0.0030.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.026
GPT teacher head0.189
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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