Desirable Characteristics of Alternative Educators in Thai Social Reform
Bibliographic record
Abstract
This study aims to explore the global megatrends and the chronic problems of Thailand for studying the roles of alternative educators that can be a part in Thai social reform; then identify the desirable characteristics which can perform these roles. The study conducts as both qualitative and quantitative methods with experts in alternative studies and human resource development in all fields. The analyses indicate that the global megatrends which will affect Thailand in the year 2021 are: aging society, urbanization, emerging infectious diseases, food insecurity, competition in technological development, world crude oil prices volatility, snatching natural resources, economic powerhouse of East Asia, global warming, climate change, regulations and good governance on environment. Thailand’s chronic problems are social inequality, unsustainable country development and education separated from learners’ way of life. The role of alternative educators in Thai social reform are to develop the new education system, fulfill the quality for Thai education and develop the learners who can effectively live and cope with the global megatrends. As a result, the desirable characteristics of alternative educators in Thai social reform are 21st century skills, knowledge in education and human resource development, professional skills, morality, leadership, thinking skills and personal characteristics. The development model for the desirable characteristics of alternative educators should be realistic, holistic and flexible for each philosophy of alternative studies. Teaching them both theoretical knowledge along with authentic experiences; in order to learn and analyze social contexts, social problems, educational problems and use education in solving those kind of problems.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".