Developing Participatory Learning Mechanism in Good Agricultural Practices Following Sufficiency Economy Lifestyle of Ban Pong Yaeng Nai School, Mae Rim District, Chiang Mai Province
Bibliographic record
Abstract
This research's objective was to develop the participatory learning mechanism in good agricultural practices following the sufficiency economy lifestyle of Ban Pong Yaeng Nai School, Mae Rim District, Chiang Mai Province. The findings revealed: 1) the overall cognition of safe cultivation by applying good agricultural practices for the lunch project was low. As the guidelines of participatory learning mechanism, the school required safe food agriculture and applied information to develop nine learning bases the Kindergarten One to Grade Sixth. The design and learning mechanism’s results were (1) a group of the participatory learning-driven working group performed the determined duties, (2) the nine learning bases with a high-level accuracy and appropriateness. Additionally, the results of driving a participatory learning mechanism for safe food were high. The overall satisfaction with the students' career skills, knowledge, and understanding of safe agriculture among the teachers and parents was high. Furthermore, the participatory learning mechanism in good agricultural practices following the sufficiency economy lifestyle of Ban Pong Yaeng Nai School, Mae Rim District, Chiang Mai Province as the possibility, satisfaction was at a high level, and the advantages were at the highest level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".