Changes and Impacts of Heritage Building to Small Hotel Building: A Case Study of Bangkok Story Hostel
Bibliographic record
Abstract
Over the last decade, trends of adaptive reuse of old buildings turned into hotels have been popular in Thailand, especially in Bangkok. Adaptive Reuse Heritage Building converted to the Hotel Building, AR-HB-hotel, effects changes and impacts in several aspects. There have been established criteria for substantial renovation, business investment and assessment of the value of heritage buildings but no concern has been paid to the surrounding community impact. The objective is to study four issues of AR-HB-hotel focused on (1) physical, (2) economic, (3) value changes, and (4) social impacts. The research method is qualitative approach with case study type. ‘Bangkok Story Hostel’ was selected as a representative case for AR-HB-hotel. This small 3-storey heritage hotel is located in a traditional trading district on Songward Street, Sampanthawong district, Bangkok. For the data collection, building history and project background, as well as physical and economic changes were collected from secondary data, surveys, and in-depth interview. Value change and social impact were collected from in-depth interview and questionnaire. The results showed that the level of physical, economic, and value changes of this building rose up, while the level of social impact stays the same or decreased a little less than the level before the renovation. This research may not be generalized wholly to another case with a significantly different context, but the four main approaches for examining physical, economic, value, and social issues and the procedure used in this study can be a guideline for future studies of AR-HB-hotel’ changes and impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".