SPATIAL FORMATION AND TRANSFORMATION OF SHOPHOUSE IN THE OLD CHINESE QUARTER OF PATANI, THAILAND
Bibliographic record
Abstract
This paper is a part of research entitled "Formation and transformation of Shophouse in the Old Town Areas, Thailand". The paper aims to clarify spatial formation and transformation of shophouse in the old Chinese quarter of Patani city, as a series of papers to discuss the formation of shophouse in Thailand. Chinese quarter in Patani is formed by the two basic road systems; roads running parallel with the river and roads running perpendicularly to the river, setting oblique gridiron pattern by eight blocks of irregular shape. According to the development of these road patterns, shophouses are built along the road-side and gradually expanded from the Chinese settlement to the present business center on the southeastern area of the quarter. The paper begins with a brief summary of Patani early history down to 5^ century but the main content is devoted to the history since 16^ century, then explicates formation and typology of shophouse in the old living quarter of Chinese immigrant, formed in the first half of 19^ century. The study aims to clarify the formation process of Patani focusing on the old Chinese settlement. It discusses the typology of shophouse and its relationship with the development and expansion of the city as well. Clarification of shophouse spatial organization and its transformation are also the main objectives of the research.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".