IDENTIFYING CORE VALUES WITH A COMMUNITY PARTICIPATORY APPROACH FOR CONSERVATION PLANNING OF URBAN HISTORIC DISTRICTS IN VIETNAM: THE CASE STUDY OF HANOI ANCIENT QUARTER
Bibliographic record
Abstract
In order to ensure an effective preservation of urban heritage in historical areas, it is critical that a science-based conservation methodology be applied, taking into account various aspects of the development process.The first key task is to identify the core values of the area, tangible and intangible values alike, while the development work will seek solutions to restore lost values, preserve other existing values and strengthen core values in a new context.The Ancient Quarter in the heart of Hanoi city has been selected as the case study for identifying core, both tangible and intangible, values of historical urban areas with community participation approach.Such findings reveal a relatively sophisticated and inter-linked combination of various elements including urban morphological structure, historical street network and architecture, commercial space, indigenous knowledge in traditional business and social management, folk festivals and traditional gastronomy.The application of the community participatory approach in identifying and analyzing core values of historical districts made the results more comprehensive and meaningful.The community plays an instrumental role in proposing a value-based conservation process while consolidating the visions and major strategies for developing historical districts in a sustainable manner.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".