Land Use Conflicts and Governance Solutions: The Case of Pulau Pangkor, Malaysia
Bibliographic record
Abstract
Pulau (Island) Pangkor is a famous tourist destination island in Malaysia due to the presence of striking natural and cultural resources. Research reports seem to indicate that Pulau Pangkor is experiencing land use challenges due to the impact of a population increase and growth in tourism activities. Present urban development is concentrated in the main town area (near Pangkor Jetty) and follows the main east-west axis. Basically, local residential development is in the eastern corridor, while the western corridor concentrates more on the development of tourism facilities. Organic growth has given rise to the development of unplanned built-up areas. Concerns have been expressed towards the occurrence of land use conflicts and limits to the island’s carrying capacity. The aim of the article is to explore these concerns and propose possible governance solutions for the development challenges on the island. Land use composition and trends are initially discussed, followed by examination of certain outstanding issues. Secondly, the existing development planning and control system is scutinized. In contrast to the mainland Manjung District, future development on the island requires more systematic management. To that effect, a possible land use governance framework as policy is outlined which could enhance the existing development processes further.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".