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Record W7128520403 · doi:10.64903/1480-6800.23.2.152

Land Use Conflicts and Governance Solutions: The Case of Pulau Pangkor, Malaysia

2020· article· W7128520403 on OpenAlexvenueno aff
Ng Ming Yip, Jamilah Mohamad

Bibliographic record

VenueArab world geographer · 2020
Typearticle
Language
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsnot available
Fundersnot available
KeywordsTourismCorporate governanceLand useMainlandPopulationPopulation growthLand developmentUrban planning

Abstract

fetched live from OpenAlex

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.209
Teacher spread0.190 · 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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