MétaCan
Menu
Back to cohort
Record W2995287168 · doi:10.25904/1912/2891

Is There a Better Approach to Providing Urban Greenspace?

2019· dissertation· en· W2995287168 on OpenAlexfundaboutno aff
Christine M Boulton

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
FundersDepartment of Education and TrainingSimon Fraser UniversityGriffith UniversityAustralian Government
KeywordsEnvironmental planningDilemmaGovernment (linguistics)Corporate governanceUrban planningLocal governmentBusinessGeographyEnvironmental resource managementPolitical sciencePublic administrationEngineeringCivil engineeringEconomics

Abstract

fetched live from OpenAlex

Researchers have found that greenspace provision (e.g. parkland) is vitally important for meeting the social, economic and environmental needs of urban populations globally. The international literature on park provision identifies many factors that influence a municipality’s ability to provide adequate parkland including political agendas, governance tools and resources. While the demands for greenspace in cities are well documented and understood amongst scholars, there is an apparent gap in scholars’ appreciation of the factors that impact supply. This thesis critically examines the challenges for cities in providing adequate greenspace to support urban populations, with a focus on local government. There is a growing recognition that parks cannot meet all residents’ needs so other types of urban greenspaces are increasingly required for this purpose. This is a dilemma for municipal greenspace planners globally. Many cities, in the face of competing economic, social and environmental demands, continue to experience a widening gap between planned and actual provision of parks. This research aims to address an important question: Is there a better approach to providing urban greenspace? Three inter-related sub-questions that help unpack this enquiry are therefore (i) What are the factors that shape urban greenspace provision and how do they operate in different contexts? (ii) What are the current (traditional and emerging) approaches to providing municipal greenspace? And (iii) are the current approaches used to provide urban greenspace effective? With rapid urban growth and land use intensification set to continue and competing demand on municipal local government budgets increasing globally, now is a critical time to evaluate the effectiveness of the current approach to planning municipal greenspace in cities. This research mobilises findings from the literature, and two case studies on municipal greenspace provision from Australia and Canada, to identify and develop improved approaches for urban greenspace provision. Analysis reveals an increasing tension for local government between clinging to traditional planning standards for open space provision, and responding to escalating expectations from business and residents alike. Leadership and more flexible approaches to providing greenspace are essential to facilitate closing the gap between the limited resources available, affordability and sustainability, and the increasing demands for urban greenspace to deliver social, environmental and economic outcomes and benefits. This thesis offers insights from grounded research to help inform future urban policy and research directions for urban greenspace planning approaches and practice. It makes an original contribution to existing knowledge of urban greenspace provision and the methods applied to urban greenspace research. Crucially, it presents the challenges faced by local government managers and planners with supplying urban greenspace, and from an insider’s perspective.

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)0.013
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0090.030
Scholarly communication0.0220.039
Open science0.0040.015
Research integrity0.0090.023
Insufficient payload (model declined to judge)0.0210.006

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 head0.120
GPT teacher head0.361
Teacher spread0.241 · 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 designObservational
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
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueGriffith Research Online (Griffith University, Queensland, Australia)Same topicUrban Green Space and HealthFrench-language works237,207