Knowledge Management in Local Wisdom of Mor Hom Natural Dyed
Bibliographic record
Abstract
The purpose of this study was to investigate Mor Hom natural dye using a qualitative research approach and local knowledge. Relevant primary and secondary data were collected through in-depth interviews with three sets of key informants, including community scholars, natural-dyed Mor Hom clothing businesses, and academics, and a participatory observation procedure involving a total of 15 participants from five local wisdom areas in Phrae province, Thailand. The content analysis revealed that the natural dye Mor Hom is inherited from the progenitors through communion with the inheritors through narrating, remembering, following, trial and error, and testing until completed. In order to determine Mor Hom local knowledge from the respondent’s implicit information, knowledge management technologies are required. In wisdom management, knowledge storage is demonstrated and organised using a method of learning by practise, learning from previous teachings, and studying from successful individuals in natural dyeing, or “best practice”, to generate explicit knowledge. In addition, they were developing a knowledge management guideline for natural dyeing wisdom that the community could use to learn and share information. In addition, the local perception of the district is extremely conservative because Mor Hom represents Phrae when people discuss this region. For the community and all concerned parties, the outcome of this research is a handbook and an electronic book for managing the local knowledge of Mor Hom natural dyes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".