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Record W2588540410 · doi:10.3130/aija.70.1_12

SPATIAL FORMATION AND TRANSFORMATION OF SHOPHOUSE IN THE OLD CHINESE QUARTER OF PATANI, THAILAND

2005· article· en· W2588540410 on OpenAlexaboutno aff
Nawit Ongsavangchai, Shuji FUNO

Bibliographic record

VenueJournal of Architecture and Planning (Transactions of AIJ) · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Crisis of the 21st Century
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)TypologySettlement (finance)GeographyHistoryArchaeologyBusiness

Abstract

fetched live from OpenAlex

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^<th> century but the main content is devoted to the history since 16^<th> century, then explicates formation and typology of shophouse in the old living quarter of Chinese immigrant, formed in the first half of 19^<th> 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.212
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Architecture and Planning (Transactions of AIJ)Same topicFinancial Crisis of the 21st CenturyFrench-language works237,207