Capturing the transition of historic urban landscapes using scores from Hanoi’s ancient quarter
Bibliographic record
Abstract
Asian cities are exposed to dynamic changes in their historic urban landscapes (HULs). However, the ways in which to quantify the degree of landscape change to guide spatial management have remained underexplored. Taking rapidly changing Hàng Buồm Street, Hanoi, Vietnam, as case study, we developed a numerical score for HULs to capture change tendencies. From onsite surveys, we defined the intensity and speed of change in trade and façades and identified vulnerable spots by mapping the scores for 131 units in 2015, 2017, and 2019. Our results showed that superficial façade changes happened regardless of the degree of trade change, and that the intense façade and trade changes happened around junctions with heavily touristified streets. We also found that aside from conventional tourism, newly emerging nighttime economies, wellness tourism, and multifunctional complexes had more intense façade changes. Based on these findings, we propose refining current management strategies by preparing stricter guidelines to control the aforementioned highly influential trades, establishing urban design project for old street to improve management of junctions especially with heavily touristified streets, and providing detailed examples of appropriate signboards that can easily be referenced by merchants and investors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".