MétaCan
Menu
Back to cohort
Record W2573732719 · doi:10.2495/sdp160311

The urban green space provision using the standards approach: issues and challenges of its implementation in Malaysia

2016· article· en· W2573732719 on OpenAlexaboutno aff
M. R. Maryanti, H. Khadijah, A. Muhammad Uzair, M. A. R. Megat Mohd Ghazali

Bibliographic record

VenueWIT transactions on ecology and the environment · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Green Space and Health
Canadian institutionsnot available
Fundersnot available
KeywordsUrbanizationEnvironmental planningUrban planningSpace (punctuation)Urban densityConsistency (knowledge bases)Space policyKuala lumpurPopulationUrban green spaceBusinessCompact cityRegional scienceEconomic growthCivil engineeringGeographyComputer scienceEngineeringEconomicsSociology

Abstract

fetched live from OpenAlex

Standards approach is conventionally used to attain consistency and certainty in urban green space planning.It has been widely used in the United States, Canada, the United Kingdom and Australia since 1920.However, in 1970s, the standards approach received wide criticism and some questioned the relevancy of the approach in high density cities.Most of the local authorities that faced development pressures often failed to achieve the standards due to limited urban spaces and land scarcity.In Malaysia, the National Urbanization Policy has set the standards of 2 hectares per 1000 population by the year 2020.However, due to high urbanization rate and increased densification, some cities, particularly Kuala Lumpur and Penang, are facing difficulties in achieving the target specified in the policy.Therefore, this paper attempts to review the broad literature on the implementation of urban green space provision using the standards approach, the issues and challenges of its implementation in urban green space planning from Malaysian perspectives.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.522
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.261
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 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

Citations70
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueWIT transactions on ecology and the environmentSame topicUrban Green Space and HealthFrench-language works237,207