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Record W7014310793

A Place to Grow? A Comparative Content Analysis of London and Toronto Ontario and the Importance of Public Green Spaces for Community Well-Being During and After COVID-19

2021· dissertation· en· W7014310793 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsOvercrowdingPublic spaceUrban planningPublic healthEquity (law)Community resiliencePlacemakingCommonsBuilt environment
DOInot available

Abstract

fetched live from OpenAlex

Public green spaces represent key aspects of our communities for a variety of reasons. Evidenced through decades of planning scholarship, well established, attractive, and accessible public green spaces can promote community health and wellbeing while supporting other elements of healthy cities, like climate resilience and adaptation. However, when the coronavirus pandemic caused the safety of these spaces to come into question for fear of community transmission, questions regarding the accessibility, availability, and equity aspects of their planning and design came to the surface as well. The coronavirus had profound influence on the demand for public green space as necessary amenities and services had been shuttered, and those who lacked private yard space in denser urban communities longed for an escape from prolonged stay at home orders. As our public health and safety came to odds with each other, these areas began to populate, and cities struggled to grapple with overcrowding in public parks and open spaces. This illustrated systemic gaps that have been deeply ingrained in planning policy and practise for years when it comes to the adequate balance between the dispersion of these spaces and the densification of urban areas like London and Toronto. Historically, planning has come to focus on these spaces as a luxury for white affluent communities and a selling point for prime real estate while other vulnerable communities go underserved and lack access to safe, accessible, and attractive public green space. The findings demonstrate how the urban development has continued to exacerbate inequities in cities by facilitating a disregard for the importance of public green spaces in communities. This study found that this is due to a lack of policy direction and support in addition to a rationale behind public green space planning that does not take a holistic approach to aesthetics, luxury, climate resilience, and public health. This research showed how cities like London and Toronto have not prioritized a balance between space and density while they continue to rapidly grow and urbanize. By comparing both a mid and large sized city, this study was able to draw similarities and difference across urban contexts by focusing on the priorities and strategies for public health, growth, and public green spaces employed by either location. In the final phase of research, the study looked to world renowned examples for green planning like Vancouver and Copenhagen to identify applicable strategies that could work in either location. The results of these findings give key recommendations for how municipalities address this balance in post COVID-19 recovery. These recommendations consider the reprioritization of public green space in planning and practise to support a holistic approach to urban development, the establishment of clear definitions for the varying types and sizes of these spaces, a measurement to understand how much greenspace exists at a micro level, and a need for development applications to respond to that measurement. These results indicate that the pandemic set off red flags for unbalance between space and place in dense urban centres yet provides a unique opportunity to move away from decades of poor planning decisions in the future.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.165
Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.010
Science and technology studies0.0170.010
Scholarly communication0.0070.004
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.243
Teacher spread0.213 · 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
Published2021
Admission routes1
Has abstractyes

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