Development of Administrative Model for High-Performance Organization of Primary Educational Service Area Office Chankrit Namchaidee, Chaiyuth Sirisuthi & Pha Agsonsua1
Bibliographic record
Abstract
The aim of this R&D research was to create an administrative model for developing a primary education office area toward a high-performance organization by assessing the current situation and desirable conditions of primary education service area offices and identifying the need for developing the management model. A management model was then created and developed; and finally, its implementation was evaluated, with recommendations for improvement. The model, a result of mixed-methods research, comprised eight components that could help primary education service area offices become high-performance organizations, with respective priorities as follows: 1) leadership, 2) strategic planning, 3) organization structure and work processes, 4) human resource management for high potential, 5) data and information technology management, 6) stakeholder orientation, 7) learning organization, and 8) productivity and outcome-based orientation. The current situation of office administration in primary education areas was at a high level, while the desirable condition overall was at the highest level. The developed model was assessed and affirmed by experts as feasible, appropriate, and useful at a high level. When considering the utility of the management model after the implementation, the overall level was also high, and satisfaction with the implementation was also very high. The model can be applied in other office areas depending on their unique conditions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".