Strengthening Collaborative Leadership for Thai Primary School Administrators
Bibliographic record
Abstract
<p class="apa">The objectives of this research were: 1) to investigate the elements and indicators of collaborative leadership of primary school administrators, 2) to explore the existing situation and required situation of collaborative leadership of primary school administrators, 3) to develop a program to enhance collaborative leadership of primary school administrators, and 4) to investigate the effect of development for collaborative leadership of primary school administrators, from the usage of developed program. Research and Development (R&amp;D) was employed which designed 4 stages, a sample group of 753 primary school administrators and teachers, chosen by multi-stage sampling, gave quantitative data; and experts purposively chosen were asked to provide qualitative input. The statistics used in this research included the percentage, mean, standard deviation, the Priority Need Indicator (PNI <sub>Modified</sub>), and Dependent t-test. The results found that 7 elements 65 indicators. The training program which was developed and reviewed consists of four modules: Module 1, Characteristics of trust and commitment; Module 2, Paradigms of shared vision and collective decision making; Module 3, Skills in transforming change, risk taking and conflict management; Module 4, Assessment and reflection on collaborative leadership influences in fulfilling duties. In the implementation of the training program for 12 weeks employing 8 training kits, the 30 primary school administrators who volunteered to join significantly improved their test scores after the training and felt very highly satisfied with the program. In addition, the collaborative leadership of primary school administrators posttest was at higher level than the pretest at .01 level.</p>
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".