The Study Elements and Indicators of Risk Management System for Secondary Schools in Thailand
Bibliographic record
Abstract
The purposes of this research aimed 1) to study the elements and indicators of risk management system for secondary schools in Thailand. 2) to study suitable the elements and indicators of the risk management system for secondary schools in Thailand. 3) to study the results of CFA (Confirmatory Factors Analysis) risk management process of risk management system for secondary schools in Thailand. There were four steps as follows: Stage 1 to study theories and concepts from the documents and research. Stage 2 to synthesize the study of concepts, principles and theories involved in this research from the stage 1. Stage 3 to evaluate and confirm the suitability of the elements and indicators of risk management system for secondary schools in Thailand by 9 experts. And Stage 4 to analyze the elements and indicators of risk management process of risk management system for secondary schools in Thailand by CFA (Confirmatory Factor Analysis). There were 350 samples in this study. The instruments used in this study were questionnaire for 9 experts and questionnaire for CFA. The statistics used for data analysis were mean, standard deviation, Chi-square, GFI, AGFI, SRMR, RMSEA. The findings found that the risk management system for secondary schools in Thailand had four main elements as follows: 1) Input consisted of 5 sub-factors, 13 indicators, 2) Process had 2 dimensions: 2.1) Dimension of the risk management process consisted of 8 sub- elements, 31 indicators, 2.2) Dimension of school burdens consisted of 4 sub-elements, 72 indicators, 3) Outputs and Outcomes consisted of 2 sub-elements, 6 indicators and 4) Feedback consisted of 2 indicators. The results of evaluation and confirmatory the suitability the elements and indicators of risk management system for secondary schools in Thailand by 9 experts found that it was suitable in every items. And the results analyze CFA of the elements and indicators risk management process of risk management system for secondary schools in Thailand revealed that the model is harmonious with empirical data and in ‘good’ 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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".