Development of Student Citizenship Indicators in Northeast of Thailand
Bibliographic record
Abstract
The objectives of this research were to develop student citizenship indicators and to validate the consistency between models of student t citizenship indicators and the empirical data. 470 samples were drawn from the population of teachers of schools under the Office of the Basic Education Commission in Yasothon Province in the Northeast of Thailand. The research instrument for data collection was a constructed questionnaire. Basic statistical data was analyzed through a statistical package whereas first and second-order confirmatory factor analyses were done through LISREL 8.53. The results were as follows. 1. Student citizenship with key indicators from the confirmatory factor analyses was totally consisted of 6 models, 20 factors, and 54 indicators. The 6 models are 1) Model of responsibility with 4 factors and 12 indicators, 2) Model of equality with 3 factors and 7 indicators, 3) Model of respect for the rights of others with 3 factors and 8 indicators, 4) Model of public mind with 4 factors and 11 indicators, 5) Model of knowing one’s roles and responsibilities with 3 factors and 8 indicators, and 6) Model of rights and freedom with 3 factors and 8 indicators. 2. These 6 models of the student citizenship indicators were consistent with the empirical data.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".