Establishing Citizen Democratic Networks to Promote Political Participation in Schools under the Provincial Administrative Organization in Mahasarakham
Bibliographic record
Abstract
This research focused on the mechanism of establishing citizen democratic networks to promote political participation in schools under the provincial administrative organization in Mahasarakham. This research aimed to 1) analyze the model of creating a civil-democratic network between universities and schools, and 2) study the process of promoting students’ political participation in schools under the provincial administrative organization in Mahasarakham. The qualitative research methods were conducted by document study and then in-depth interviews with the participant observation method. Research instruments were 1) the related documents and research in a reliable database, and 2) four interview questions for each group and the participant observations form. Collected data were analyzed and categorized into critical issues and themes based on the literature. The participants comprised sixty-two interviewees and were divided into four groups: 1) Two representatives of the Office of Education, Religion and Culture of the provincial administrative organization; 2) Twenty school administrators under the provincial administrative organization; 3) Twenty teachers responsible for the civics course, and 4) Twenty high school students. The findings revealed: 1) The model for creating a civic network between universities and schools. There must be a central person in the management position with the authority to command leadership and technical communication between the school and the university. The creation of a network of citizens of democracy will be successful. 2) The process of creating the political participation of students in schools arose from the process of building democratic citizenship through school education and extra-curricular activities. Students can then apply that knowledge in daily life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".