Indicators of Good Governance for Administrators of the Primary Educational Service Area Office
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it