Exploring Dimensions of Sustainable Housing Development: Results from a Pilot Study in Pulau Pangkor, Malaysia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".