Miang Culture: The Community Resources Management Through “Design-Based Learning” for Self-Reliance of Highland Communities in the Upper Northern Thailand
Bibliographic record
Abstract
The purpose of this article is to describe the formation of Miang resource management of the highland communities in the Upper Northern Thailand through design-based learning which is the mechanism to learning the management of the communities’ resource for self-reliance derived from the solid and strong foundation of the communities. The method utilized in the quality research, collecting the information from the documentary study, participatory and non-participatory observation, and the deep interview with the community philosopher, and the data is analyzed by using the content analysis method. The research found that most of the highland communities in the Upper Northern Thailand located at the west of Phi Pan Nam Mountains have a lifestyle that connects with the participatory Miang resource management, have wisdom which is the innovation of design-based learning for self-reliance of the communities through the accumulating and the transfer of the knowledge from generation to a generation called “Miang Culture” which is created from the systematic design-based learning process through the wisely utilization of Miang resource existing in the community, and to cause the maximum sustainability based on the participation of the community without causing the trouble or breaching other’s right.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 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".