Development of Leadership Components in VUCA World for Primary School Administrators
Bibliographic record
Abstract
The objective of this research was to investigate, analyze, and verify the consistency of leadership components in the VUCA world for primary school administrators under the Primary Educational Service Area Office in Thailand’s 13th inspection area, based on empirical data. The study involved 8 experts and a sample of 400 school administrators selected by stratified random sampling based on the number of schools in each educational service area. Research tools included an interview form and a 5-level estimation scale questionnaire with a reliability of 0.989. Data were analyzed using content analysis and confirmatory component analysis methods. The results revealed that leadership in the VUCA world for these school administrators comprised 4 main components, 10 sub-elements, and 94 indicators. These elements were consistent with empirical data, with standard component weight coefficients for all key components statistically significant at the 0.01 level. The main components with the highest standard weights were change creation (bsc = 0.97), collaboration (bsc = 0.96), resilience and adaptation (bsc = 0.93), and vision (bsc = 0.92). The accuracy coefficient of all key elements, measured by R², indicated a high level of covariance in the leadership component model for school administrators in the 13th inspection area (R² between 0.84 and 0.95).
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".