Scenarios of Thailand Secondary Education within B.E. 2570
Bibliographic record
Abstract
The objectives of this research were: 1) to study the current situation and problems of Thailand secondary education 2) to propose the Scenarios of Thailand Secondary Education within B.E 2570 by using EDFR (Ethnographic Delphi Futures Research) The 1st Phase: the study of current situation and problems of Thailand secondary education. The 2nd Phrase: the four steps of EDFR; Step 1 (1st round of EDFR) expert interviews using a semi-structured interview, summary of possible trends by using NVivo. Step 2 (2nd round of EDFR) trend analysis by using 12 expert questionnaires to find out Medians and Inter Quartile Range. Step 3 (3rd round of EDFR) the expert verification and consensus by using 12 expert questionnaires to find out Medians and Inter Quartile Range. Step 4 writing Scenarios of Thailand Secondary Education within B.E 2570. Research results showed that there were four aspects of current Thailand Secondary Education ; Learning and teaching aspects, Educational personnel aspect, Budget aspect, and Educational administration aspect. The Scenarios of Thailand Secondary Education within B.E 2570 would be 1) 12 possible trends of Learning and teaching aspects 2) 11 possible trends of Educational personnel aspect 3) 6 possible trends of budget aspect and 4) 10 possible trends of Educational administration aspect.In summary, the Scenarios of Thailand Secondary Education within B.E 2570 according to this research could be conclude for the preparation of educational policy in secondary education which accord to educational plan of the office of education council to develop education with high efficiency and internationalize.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".