The 21st-Century Professional Leadership Standards of Secondary School Administrators in Nakhon Nayok, Thailand
Bibliographic record
Abstract
The 21st Century has brought a lot of challenges in developing the professional leadership characteristics of school leaders. Their roles are no longer limited in implementing educational policies and objectives but have become responsible for raising the generations and qualifying them in a rapidly changing era. However, in Thailand, it was observed that there were Thai principals and school leaders who were not adequately trained for school leadership. This descriptive research surveyed the profile of the school administrators of secondary schools in Nakhon Nayok, Thailand as well as their level of practice of the 21st-Century professional leadership standards. A questionnaire checklist adapted from the frameworks of Kelly Lambert (2001) and The Wallace Foundation (2013) was used to gather data. Data analysis showed that the school administrators are females, with master’s degrees, 55 years and older, and have few years of administrative experience. Further, they highly practiced the different 21st-century professional leadership standards; however, these were not influenced by their profile. Lastly, it was found out that there were no significant differences in the level of practice of the different 21st-century professional leadership standards as indicated by the profile indicators of the school administrators.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".