The Scenarios of Educational Administration of Secondary Schools in Thailand During the Next Decade (A.D. 2022-2031)
Bibliographic record
Abstract
This research is the scenarios of educational administration of secondary schools in Thailand during the next decade (A.D. 2022-2031) by using EDFR research techniques, The research objectives were 1) To study the current conditions and problems of educational administration of secondary schools in Thailand 2) To study the scenarios of educational administration of secondary school in Thailand during the next decade (A.D. 2022-2031). The research results can be summarized as follows: I) The current state and problems of educational administration of the secondary school in Thailand overall practice is at a high level when considering each aspect, there are practices at a high level in all 8 aspects: 1) Planning and quality assurance 2) Academic affairs 3) Student affairs 4) Human resources 5) Administrative work 6) Finance and supplies 7) Building and environment services and 8) Community work and affiliate networks. As for the educational administration problems in Thai secondary schools, the overall practice is at a low level, when considering each aspect, there is a low level of practice in all 8 aspects: 1) Planning and quality assurance 2) Academic affairs 3) student affairs, 4) Human resources, 5) Administrative work, 6) Finance and supplies, 7) Building and environment services, and 8) Community and affiliate networks. II) The scenarios of educational administration of the secondary school in Thailand during the next decade (A.D. 2022-2031) have 49 possible trends as follows: 1) Planning and Quality assurance 7 trends 2) Academic affairs 11 trends 3) Student affairs 8 trends 4) Human resources, 9 trends, 5) Administrative, 4 trends, 6) Finance and supplies, 5 trends, 7) Building and environment services, 3 trends, and 8) Community and affiliate network 2 trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".