Development of a Spiritual Leadership Training Courses for Religious in Preparing to Be Educational Administrators in Thailand
Bibliographic record
Abstract
The objectives of this study were to: 1) construct a training course of spiritual leadership for those preparing to become administrators in Catholic schools in Thailand; 2) implement and utilize the training course, and 3) analyze, evaluate and enhance the training course. The sample used in this research was 32 young participants who were preparing themselves to be the administrators of Catholic schools in Thailand. The research steps were: 1) the requirement assessment, 2) training course development, 3) the training course experiment, and 4) training course optimization. This is a research and development project. The methodologies used in this study were the cognitive and attitude test, semi-structured interviews and the suitability evaluation form of the training course. The findings of this study can be summarized as follows: 1) The training course in spiritual leadership to prepare administrators of Catholic schools in Thailand consists of five modules: 1. Towards a theory of spiritual leadership, 2. Vision, 3. Altruistic love, 4. Faith and hope, and 5. Example of success; 2) the results of the experiment revealed that after completing the training course, the religious participants who had prepared themselves to be Catholic school administrators in Thailand got higher scores on cognitive and attitude tests, with total scores in the evaluation reaching 4.76 (the highest level); 3) the training course was improved by extending the training session from two days to three days, and conducting a bilingual PowerPoint presentation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".