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Record W2332202099 · doi:10.3130/aija.76.779

TOURIST-DEPENDENT ADAPTIVE REUSE IN THE OLD RESIDENTIAL QUARTER OF MELAKA CITY, MALAYSIA

2011· article· en· W2332202099 on OpenAlexaboutno aff
Rhan See Chua, Atsushi Deguchi

Bibliographic record

VenueJournal of Architecture and Planning (Transactions of AIJ) · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Adaptive reuseReuseTourismBusinessEnvironmental economicsGeographyCivil engineeringEngineeringEconomics

Abstract

fetched live from OpenAlex

This paper presents findings from the study of tourist-dependent adaptive reuse in the old residential quarter of Melaka City. Two surveys have been conducted in this study. The first survey involves the collection of building and land use data via non-participatory observation method. While the second survey uses a questionnaire to measure the perceptions of different users of the old quarter, namely local community which consist of residents and business owners, and also domestic and international tourists, on the effects of adaptive reuse. Findings show that almost 25 percent of the actively used buildings are housing tourist-dependent uses (TDUs), which are concentrated mainly on Jonker Street and its surrounding area. A number of streets have more TDUs buildings than non-TDUs, and some are dominated by one type of TDU only. This shows that tourist-dependent reuse activity in the old quarter needs to be checked to minimise future negative implications. This study first classifies adaptive reuse approaches in the old quarter and relates them to the different effects they bring to the old quarter. Then the concerns and expectations of different user groups in the old quarter towards the physical, social, economical and tourism effects of adaptive reuse are identified. Finally, this paper proposes planning and area management measures to regulate adaptive reuse in the old residential quarter.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.412

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.075
GPT teacher head0.238
Teacher spread0.162 · 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 designQualitative
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

Citations10
Published2011
Admission routes1
Has abstractyes

Explore more

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