Heritage Revitalisation Strategy: The Saviour of Petaling Street
Bibliographic record
Abstract
In revitalising heritage cities, Ross (2024) criticised the dominancy of urban preservation and management in cities that focus more on tangible than intangible heritage. The urban law highlighted that ‘image and identity’ are essential to represent a morphology and tangible and intangible heritage of specific urban avenues. Xiang & Mohamad (2023) unveiled that the front and back of heritage shophouses at Petaling Streets are adaptive usage. Heritage Revitalisation Stragey (HRS) is not new to Canadian, European, Middle Eastern, Chinese and Russian cities, which the tangible and intangible heritage treasures are valued equally in a strategic approach. This study evaluates the existing Heritage Revitalisation Strategy (RS) dedicated for the local authority of Dewan Bandaraya Kuala Lumpur (DBKL) and the Think City in valorising the heritage street of Petaling. Even though various methods were implemented in most heritage streets in Kuala Lumpur which were not successful, therefore a remarkable approach of HRS shall be tested to Petaling Street to groom the public place’s ambience. This research aims to identify the best approach applicable to urban heritage streets globally that is applicable urban heritage streets in Kuala Lumpur. Researchers conducted semi-structured interviews with the heritage manager of Kuala Lumpur City Hall and Think City’s personnel, visual observations, digital photo analysis, and Focus Group Discussion (FGD) with shop owners were applied to establish a balanced strategy between the existing HRS and the Petaling Street’s version. Valorising the intangible and tangible built heritage as daily practices of the local street community as part of the street culture. Still, a proper Heritage Revitalisation Strategy (HRS) is urgently required specifically for our urban heritage street. Understanding this rare strategy for transforming into the street is critical in valorising its value. A proper RS by the DBKL management to further elevate its function as a tourist boosting factor is a catalyst for other avenues.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".