Indicators of Good Governance for Administrators of the Primary Educational Service Area Office
Bibliographic record
Abstract
This research aimed at accomplishing the following: (1) to build theoretical model, then to test its fitness with the empirical data; and (2) to investigate the factor loading of the main factors and sub-factors, as well as those indicators, which were compared to the determined criteria. The research applied descriptive research methodology to collect the data using a 5-scale questionnaire. The population consisted of 1,100 administrators in the Primary Educational Service Area Office (PEASO). The determination of the sample group size was established by applying the rule of population sample parameter proportion of 20:1, which was equal to 820 participants. From the 795 questionnaires, which were returned, the results of the data analysis were concluded by analyzing confirmatory factors using the AMOS program. It was determined that the theoretical model and empirical data were relevant given the following criteria: a Relative Chi-Square > 3.00 and a Root Mean Square Error of Approximation > 0.0. In addition, the Goodness-of-Fit Index, Adjusted Goodness of Fit Index, Comparative Fit Index, and Normed Fit Index were found to be between 0.90 – 1.00. Moreover, the factor loading of the main factors was from 0.86 – 1.06, which is higher than the determined criteria (0.70), while the factor loading of the sub-factors and the indicators ranged from 0.73 – 0.95 and 0.30 – 1.00, respectively. These numbers were also higher than the determined criteria of 0.30, indicating that as a result of the research, the theoretical model could be used as a guideline to improve better governance for the administrators of PEASO with construct and content validity.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".