The Community-based Institutional Administration Model to Promote Students’ Career Skills in Chiang Mai Education Sandbox, Thailand
Bibliographic record
Abstract
The research objectives were shown as follows: 1) to research the community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox, 2) to design the community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox, 3) to experiment the community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox, and 4) to develop the community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox by using research and development method. The samples of this study were 1) 9 basic education commissions, 2) 8 teachers and educational personnel, 3) 15 community leaders, monks, local wise men, and villagers, 4) 7 educational experts, and 5) 28 students, which in total were 67 people. The tools used in this study were as follows: 1) structured interview form, 2) community-based institutional administration model assessment form, 3) satisfaction assessment form, and 4) group discussion record form. Qualitative data were analyzed using Content Analysis and presented in a descriptive form (Descriptive Analysis), and quantitative data were analyzed using a statistical program to determine the mean and standard deviation. The result showed as follows:1) A community-based institutional administration model for promoting students’ career skills in the Chiang Mai education sandbox must be an educational management in an area with spatial diversity. School administrators and teachers must provide great cooperation and interest in participating in the development of the school by following the guidelines of the education sandbox. Furthermore, piloting basic learning activities that involved community areas and the area surrounding a community that is rich in natural resources and the environment was essential. This was the significant strength point that allowed us to develop a community-based institutional administration model more effectively.; 2) A community-based institutional administration model for promoting students’ career skills in the Chiang Mai education sandbox had an institution management strategy called the "4K Model," consisting of four strategies as follows: 1) Strategy 1 Knowingly: K1 Knowingly situations in the world, 2) Strategy 2 Keep Step: K2 Keep moving steps forward together, 3) Strategy 3 Knowledge: K3 Transferring knowledge from the community, and 4) Strategy 4 Kit out: K4 Sourcing support resources.; 3) Using the community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox, it was found that the overall level of satisfaction in both teachers and educational personnel, and students towards the use of this model was at the highest level.; 4) The community-based institutional administration model to promote students’ career skills in the Chiang Mai education sandbox that the researcher had developed to be more complete was under these five strategies as follows: 1) Strategy 1 Knowingly: K1 Knowingly situations in the world, 2) Strategy 2 Keep Step: K2 Keep moving steps forward together, 3) Strategy 3 Knowledge: K3 Transferring knowledge from the community, 4) Strategy 4 Kit out: K4 Sourcing support resources and 5) Strategy 5 Key success: K5 Key success. It was also found that there was a mechanism that supported this model, consisting of four mechanisms as follows: 1) policy mechanism, 2) academic cooperation building, 3) collaborative vision building, and 4) network party.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".