Local Wisdom as a Planning Strategy and Sustainable Settlement Development in ToKaili Traditional Settlement, Central Sulawesi, Indonesia
Bibliographic record
Abstract
Indonesia is home to many tribes spread across many islands. It is home to local wisdom that thrives and is preserved in the community. The Kaili tribe (ToKaili) is one of the tribes that live on the island of Sulawesi which is located in the province of Central Sulawesi. This has unique local wisdom because ToKaili is one of the tribes in Indonesia that was formed due to evolutionary development starting from moving to a sedentary lifestyle, to settling which is worthy of study by many researchers in the field of architecture. An interesting part of local wisdom is their customs and traditions in organizing and designing their settlements since ancient times. The ToKaili settlement planning and design process mainly depends on how customary law regulates the phenomena of community life and shapes sustainable planning strategies. The purpose of this study is to reveal the phenomenon of local wisdom To Kaili in planning and designing harmonious sustainable settlements. This study uses phenomenology as a method for analyzing ToKaili local wisdom and finding planning strategies and designing harmonious solutions. It was found that variations or patterns are ranging from micro-scale settlements to large scale settlements that are interrelated and metamorphosed from micro-scale settlements to large scale settlements (From Sou to Ngata bete) Formed by customary rules.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".