TOURIST-DEPENDENT ADAPTIVE REUSE IN THE OLD RESIDENTIAL QUARTER OF MELAKA CITY, MALAYSIA
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".