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Public spaces in Bangkok and the factors affecting the good public space quality in urban areas

2020· article· en· W3089067407 on OpenAlexaff
Chompoonut Kongphunphin, Manat Srivanit

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsUsabilityPublic spaceQuality (philosophy)Space (punctuation)GeographySocioeconomicsComputer scienceSociologyArchitectural engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract Public spaces are an important component of the urban area used to support public activities. The aim of this study is to evaluate the quality of public spaces where subdivided into districts of Bangkok city area. A methodology is carry out preliminary site surveying in a 31 public parks based on four main good public space indicators, which including accessibility, imageability, usability and sociability. In this study, the Z-score or standard score is defined as a statistical measurement of the associate of site characteristics score, has been apply to determine quality of public spaces. The results reveal that the all of public spaces are outstanding scores in terms of imageability and usability, and their Z-score values are consistently coherent, especially the high-quality public spaces located in the inner zone of Bangkok. While accessibility and sociability factors are poor quality, their values show on some public spaces that mostly located in the middle and in the outer zones of Bangkok. These results can be helpful to given a priority when managing city facilities in the Bangkok’s urban space and maintaining or improving its public space value.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.235
Teacher spread0.189 · 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 teacher head, 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

Citations4
Published2020
Admission routes1
Has abstractyes

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