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Record W2740578572 · doi:10.21837/pm.v15i2.272

THE IMPACT OF GENTRIFICATION ON LOCAL URBAN HERITAGE IDENTITY IN OLD QUARTER, MELAKA HERITAGE CITY

2017· article· en· W2740578572 on OpenAlexaboutno aff
Raja Norashekin Raja Othman

Bibliographic record

VenuePLANNING MALAYSIA · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsGentrificationSafeguardingLocal communityIdentity (music)Cultural heritageValue (mathematics)Quarter (Canadian coin)Quality (philosophy)Community developmentLocal economic developmentSociologyLocal DevelopmentEconomic growthRegional scienceGeographyPolitical scienceEconomicsLawArchaeology

Abstract

fetched live from OpenAlex

Gentrification represents a new trend of development towards the new forms of socio-spatial divisions of the city centre. It also restores the quality of urban development and life of the local community. However, there are various issues arising from this developmental process. These issues have been identified as the demand for physical development, economic worth and sociological evaluation of the community, which have an effect on the local identity of the study area. Therefore, the aim of this paper is to identify the impact of gentrification on the local urban heritage identity in the heritage city of Melaka. The study takes a quantitative research approach. This study also shows the positive and negative impacts to the local communities, and these effects may differ with other cities. The study evaluates the impact on the socio-culture and spatial structure in this area. Hence, recommendations of this paper place emphasis on the involvement of the local community in determining the direction of development. Apart from that, safeguarding the local intangible cultural heritage value in the urban development process should also be emphasised as it is intended to respect and protect the rights of the local community while creating a balanced development without compromising quality of cultural heritage assets of the historic city.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.089
GPT teacher head0.305
Teacher spread0.216 · 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

Citations8
Published2017
Admission routes1
Has abstractyes

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