Linear Structural Relationship Model of Servant Leadership of School Administrators Affecting Effectiveness of Primary Schools in The Northeast
Bibliographic record
Abstract
This research aims to develop a structural relationship model of servant leadership among educational administrators that impacts the effectiveness of primary schools in northeastern Thailand and to examine the model’s alignment with empirical data. The study is conducted in two phases: Phase 1 involves developing a structural linear model of servant leadership among educational administrators that affects the effectiveness of primary schools in northeastern Thailand, and Phase 2 tests the model’s consistency with empirical data. The sample consists of 500 administrators and teachers from the 2021 academic year. The data collection instrument is a rating scale questionnaire measuring school effectiveness, with discriminative power values between 0.44 – 0.81 and reliability of 0.98, and a servant leadership factor with discriminative power values between 0.22 – 0.87 and reliability of 0.98. Data analysis includes frequency, percentage, mean, standard deviation, Pearson correlation coefficient, and structural linear modeling using specialized software. The research findings indicate that: 1) The structural linear model of servant leadership among educational administrators impacting primary school effectiveness in northeastern Thailand includes five dimensions: Vision, with three observable variables; Awareness, with three observable variables; Understanding and Valuing Others, with three observable variables; Staff Development, with three observable variables; and Service, with five observable variables. School effectiveness comprises four observable variables. 2) The developed model is consistent with the empirical data, with a Chi-square (χ²) value of 159.66, degrees of freedom (df) of 137, a p-value of 0.09, a relative Chi-square (χ²/df) of 1.17, RMSEA of 0.02, GFI of 0.97, and AGFI of 0.95.
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".