The Leadership Enhancement in Education 4.0 School Administrators
Bibliographic record
Abstract
The objectives of this research were: 1) to study the components, 2) to study the current situation and the desirable conditions, and 3) To assess needs for enhancement of leadership of school administrators in Education 4.0 under the Office of Secondary Education Service Areas in the Northeast region. The sample group included schools under the Office of Secondary Educational Service Areas in the Northeast region. The data were collected from 15 school administrators, 365 teachers, with the total number of 380 persons by using Stratified Random Sampling. The instruments used in this research were component evaluation form and questionnaire. The statistics used for data analysis comprised percentage, mean, standard deviation, reliability and PNI modified. The research results were found that: I. The components for leadership enhancing in education 4.0 professional administrators consisted of 7 components as follows: 1) Knowledge and ability, 2) Leadership skills, 3) Academic skills, 4) Morality and ethics, 5) Modern skills, 6) Characteristics, and 7) Results of performance. II. The current situation for leadership enhancing in education 4.0 professional, in overall, was rated at a moderate level, the desirable conditions, in overall, were rated at the highest level. III. The priorities of needs assessment are 1) Results of performance, 2) Morality and ethics, 3) Characteristics, 4) Modern skills, 5) Leadership skills 6) Knowledge and ability, and 7) Academic skills.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".