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Record W4402277397 · doi:10.35762/aer.2024040

Outlooks and Challenges for Urban Green Space Development: A Review Case Study in Thailand

2024· review· en· W4402277397 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApplied Environmental Research · 2024
Typereview
Languageen
FieldArts and Humanities
TopicArchitectural and Urban Studies
Canadian institutionsnot available
FundersChulalongkorn University
KeywordsUrban green spaceSpace (punctuation)Environmental planningGeographyRegional scienceArchitectural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Even though prior studies have provided ample proof of the benefits of urban green space, Thailand's development and exploitation of urban green space remain insufficiently efficient. To assess the prospects for urban green space development in Thailand, our review study highlighted gaps and challenges related to these areas. This review study discussed the rationale for urban green space, its definitions, as well as its benefits and co-benefits (i.e., economic, social, health, and environmental). The review research additionally discussed Thailand's current urban green space issues, associated challenges (such as difficulties valuing and using urban green space, budgetary limitations, low priority for urban green space, and poor urban green space standards), and short- and long-term green space goals. Moreover, this study reviewed the urban green space assessment criteria (e.g., quality, potential urban green spaces, planning and strategy, and location selection), tools, and intriguing green space policies and practice approaches (e.g., planting and protecting trees, increasing public parks, and city taxpayers) from other previous studies and developed countries whose cities rank in the top 10 worldwide in terms of the ratio of green space to population density, for example, the US, Singapore, Germany, Switzerland, Canada, and the Netherlands. There have also been interesting platforms and technology introductions for developing and managing urban green spaces. Finally, the review study proposed guidelines for green space development that may be beneficial for Thai policymakers to improve green space based on lessons learned from other developed countries, such as being more accessible, a proper size, an appropriate distance from neighborhood residents, having suitable facilities and equipment for the users, maintaining the beauty and cleanliness, having recreational activities, tax incentives, and advanced technology platforms. Additional research is required to examine the damage costs associated with urban green space, policy, and cost-benefit analysis to make it more practicable.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.312
GPT teacher head0.376
Teacher spread0.064 · 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