Components and Development Guideline of Active Citizenship for Secondary School Teachers
Bibliographic record
Abstract
Active citizenship is essential for promoting democratic values and social cohesion in educational settings. The purpose of this research were to investigate and analyze empirical data to propose effective guidelines for cultivating active citizenship among secondary school teachers under Thailand’s Office of the Basic Education Commission (OBEC). The study involved 475 secondary school teachers and educational personnel, selected through stratified random sampling based on the number of schools in each educational service area. Data were collected using a five-point rating scale questionnaire, which achieved a high reliability coefficient of .985, indicating excellent internal consistency. Data analysis involved content analysis and confirmatory component analysis. Results revealed that active citizenship among secondary school teachers consisted of five primary components, nineteen sub-components, and seventy-five indicators. The confirmatory analysis indicated strong alignment between the proposed model and empirical data, with all primary components showing statistically significant standardized weight coefficients (p < .05). The most influential components were participation (β = 1.00), social responsibility and empathy (β = .99), respect (β = .96), and initiative in problem-solving (β = .89). Additionally, the model exhibited high explanatory power, with coefficient of determination (R²) values ranging from .80 to 1.00. The research recommends seven practical strategies to foster active citizenship among teachers: 1) Instilling democratic values and a sense of public consciousness, 2) Promoting participation in public life, 3) Implementing integrated, learner-centered education, 4) Fostering global citizenship, 5) Enhancing teacher development and the educational system, and 6) Applying ethical principles and promoting lifelong learning. These strategies offer practical pathways for educational administrators and policymakers aiming to enhance teachers’ roles as active citizens.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".