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Record W1974938896 · doi:10.5539/ach.v7n1p84

A Synthesis of Traditional Knowledge Used in the Construction and Restoration of Thai Buddhist Ubosot in Bangkok and Its Surrounding Provinces

2014· article· en· W1974938896 on OpenAlexvenueno aff
Sumphan Phormsit, Ying Keeratiburana, Pairat Thidpad

Bibliographic record

VenueAsian Culture and History · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBuddhismTraditional knowledgeFocus groupEconomic shortageNeglectQualitative researchInheritance (genetic algorithm)Knowledge managementEngineeringBusinessSociologyGeographyComputer sciencePsychologySocial scienceMarketingArchaeology

Abstract

fetched live from OpenAlex

This qualitative investigation synthesises the use of traditional knowledge in the architecture of Thai Buddhist temples. The focus of this paper is on the main worship hall of the temple, the ubosot. The research was carried out between August 2010 and March 2013 using data collection tools of interview, observation, focus group discussion and workshop. The findings show that current restoration projects neglect traditional knowledge of ubosot construction in favour of more modern techniques. Six problems with traditional knowledge in this field were identified: lack of inheritance, insufficient funds, poor budget management, skilled labour shortages, lack of historical records and no standardisation. This paper synthesises traditional knowledge of ubosot construction and outlines five steps for the successful application of traditional knowledge in the creation and restoration of ubosot: a) planning; b) creation and restoration using traditional methods and processes; c) evaluation of construction and restoration; d) correction and improvement; e) recording of results for use as a future guide and model. These results can be used as a model for future restoration projects in Bangkok and beyond.

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)0.000
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.038
GPT teacher head0.266
Teacher spread0.227 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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