Silk Patterns: Conservation and Development of Traditional Thai silk Production for Added Commercial Value in Khon Kaen Province
Bibliographic record
Abstract
Silk patterns are examples of fine art that show the valuable culture and identity of Thai communities. This is a qualitative research and the researchers used a purposive sampling technique to identify four districts in Khon Kaen province for assessment by means of survey, observation, interview, focus group discussion and workshop. The history and development of silk patterns in Khon Kaen province occurred from a process of pattern making called mudmee. The techniques were passed from generation to generation, copying plant and animal patterns in nature. Silk patterns developed in three ways: 1) using mudmee patterns as a model; 2) imitating television, fashion magazines and other media; 3) following specific commission specifications of customers. Production processes are mudmee (silk blending) and tammee (silk marking), which require original wooden equipment reinforced with steel for strength and electrical motors for speed. Both simple silk fibres and factory silk fibres are used and patterns are created based on traditional designs. In order to develop silk pattern production for added commercial value, original patterns with contemporary character must be chosen and expanded as bigger and more varied products, such as handbags. New silk patterns sold in local and regional markets will boost the income of people in Khon Kaen Province.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 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".