Cultural Value Transformation in Traditional Market Spatial Planning in City of Denpasar, Gianyar and Klungkung – Bali, Indonesia
Bibliographic record
Abstract
The purpose of this study is to determine the cultural values that underlie the formation of traditional market spatial patterns. In this research will be studied traditional market spatial pattern, change-morphology, hidden cultural values in the form of the morphology, and the factors that cause change. This research takes place in traditional markets in the city of Denpasar, Gianyar, and Klungkung, Bali Indonesia. This research uses descriptive qualitative method with multi layer mapping technique and interview. The results of this study indicate that the morphology of traditional markets on a city scale is one of the three pillars of palace power: political, economic and cultural. At footprint scale of the traditional market morphology is a transformation of the conception of the spatial value of traditional Balinese space that places the function of the purity zones. From the beginning of the formation of the market there are some morphological changes both on the scale of the city and the scale of the site, especially with regard to the placement of the shrine. The main factors causing the change are political and cultural change. Political factors started in the fall of the castle into the hands of the invaders and the cultural factor is the community's attempt to restore the market zoning to a more appropriate traditional spatial conception.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".