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Record W7128525495 · doi:10.64903/1480-6800.23.2.169

Exploring Dimensions of Sustainable Housing Development: Results from a Pilot Study in Pulau Pangkor, Malaysia

2020· article· W7128525495 on OpenAlexvenueno aff
Ng Ming Yip, Jamilah Binti Mohamad

Bibliographic record

VenueArab world geographer · 2020
Typearticle
Language
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityPopulationSustainable developmentDimension (graph theory)Sustainable communityQuestionnaire

Abstract

fetched live from OpenAlex

Pulau Pangkor (Pangkor Island) is a famous tourist destination located in the state of Perak, Malaysia, and it is also home to an increasing number of islanders. With 70% of the island still forested, most of the resident population is concentrated in pockets of housing development in Pangkor Town, Pasir Bogak, Sg. Pinang Besar, Sg. Pinang Kecil, Teluk Gedung, Teluk Nipah and Teluk Dalam. The challenge for the future is seen as bringing about sustainable development within the housing sector. This article aims to investigate the key factors that affect sustainable housing development on Pulau Pangkor through the use of sustainability indicators that have been earlier identified through the literature review. Primary data was collected through a questionnaire survey on selected households from six residential areas/villages. There are four dimensions of sustainability assessed, namely the physical, social, environmental, and economic dimensions of housing. The study found that all four dimensions are significant predictors for sustainable housing development. The physical dimension is the main predictor of sustainable housing development in Pulau Pangkor (R 2 = 0.466, p<0.05), followed by the social, environmental and economic dimension. The research findings may be used by local officials to enhance the living conditions of the island residents through provision of sustainable housing infrastructure.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.060
GPT teacher head0.249
Teacher spread0.188 · 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.

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
Published2020
Admission routes1
Has abstractyes

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