Adaptive Reuse of Vernacular Architecture for Conservation in Indonesia: Case Study of South Nias Village, Bawömataluo Conservation Project
Bibliographic record
Abstract
Bawömataluo village, which is located in Nias Island, Indonesia, is among the most important National Cultural Properties. The village has the highest number of surviving traditional houses in one village in Indonesia (53% left – Fanitra Pedi A., 2017, p. 64). The architecture is considered to be the finest artistic masterpiece in Indonesia. The change of agriculture type in the 20th century and vast improvement of infrastructure in the 21st century cause poverty and bring difficulties in sustaining the cultural property. Villagers modify their houses by replacing some of the building materials with the ones that are more economical in price, but this contributes to the destruction of the original structure. Most Nias villagers still want to stay in their traditional houses. But, due to the maintenance funding problem and introduction to healthy houses in the 20th century, some materials were changed into industrial materials and some parts were damaged. There is an initiative to use traditional house/omo hada as a homestay program which allows villagers to have a close contact with local and international tourists and earn more income. This research observed 30 omo hada and took two samples for a deeper space understanding. This paper tries to investigate activities in the two samples of omo hada with and without homestay activities. The investigation finds out that tawolo (communal space that is located at the front of the house) is a frequently used space which occupies 40% area of the second floor and föröma (private space that is located at the back of the house) is the least used space with 60% area of the second floor which is suitable for adaptive reuse in omo hada.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".