A Study of Education Administrators’ Exertion of Authority Phitsanulok Primary Educational Service Area Office 1
Bibliographic record
Abstract
The objectives of this study were 1) to study education administrators’ exertion of authority 2) to compare education administrators’ exertion of authority under Phitsanulok Primary Educational Service Area Office 1. The variables were divided by the administrators’ experiences and the sizes of the institutions. The population in this study was 1,161 education administrators and teachers. The number of the samples was 291 determined using Krejcie and Morgan Table, stratified random sample of the sizes of the institutions, and simple random sampling. The study used a 5-point rating scale questionnaire for item-objective congruence ranged between 0.80-1.00 and the reliability was at 0.962. Data analysis statistics were mean, standard deviation, t-test statistic, one-way ANOVA test, and pair difference test by Scheffe’s method. The results were found as follows. 1) In general, the level of education administrators’ exertion of authority was high. The aspect with the highest mean was the authority of information technology access (x = 4.17), while that with the lowest mean was the authority of punishment (x = 3.56). 2) There was no difference, in general, in the comparison of education administrators’ exertion of authority divided by work experience. However, when each aspect was considered, there were statistically significant differences in the authority of punishment and the sizes of the institutions at statistically significant level of .05.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".