Indicators of Sustainable Leadership for Secondary School Principals: Developing and Testing the Structural Relationship Model
Bibliographic record
Abstract
The objectives of this study included to study the appropriateness of indicators for selection in the developed model, to examine the fitness of the developed model, and to verify the factor loading value of major components, sub-components, and indicators, respectively. Sample included 2,359 secondary school principals under the jurisdiction of the Office of the Basic Education Commission. Collecting data using a set of rating scale questionnaires were derived from 860 randomly selected proportional random sampling. Data were analyzed by using statistical program and AMOS program. The findings were corresponded to the following hypotheses: (a) The 62 indicators were suitable for the criteria as average equal to or higher than 3.00 and distribution coefficients equal to or less than 20% which were selected in the model, (b) The developed models were fitted with empirical data according to the value of relative Chi-square (CMIN/DF), root mean square error of approximation (RMSEA), goodness-of-fit index (GFI) adjusted goodness-fit index (AGFI), comparative fit index (CFI), and normed fit index (NFI) in accordance with the criteria from first and second order of confirmative factor analysis, and (c) the major components had factor loading ranged from 1.00 to 1.28, which were higher than the criterion at 0.70. The minor components had factor loading between 0.83 and 1.28. The indicators had factor loading ranged from 0.88 to 1.16, which are higher than the criterion as 0.30, respectively.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".