A Case Study of Mindful Leadership in an Ability to Develop Focus, Clarity, and Creativity of the Leader of Buddhist Higher Education Institute
Bibliographic record
Abstract
This research is a single case study using a qualitative approach that produces descriptive data consisting of written or oral words resulted from interviews and observation. The research was carried out at Buddhist Higher Education Institute, the Institute of Advanced Buddhist Studies – Plum Village Buddhist Monastery Upper Hamlet of France. The research procedures used in this case study consist of six steps of case study research by Robert K. Yin. The research data collection was obtained by purposive sampling and snowball sampling. Data analysis techniques are used through pattern matching, explanation making, and analyzing data time series. This research found that mindful leadership can develop concentration and be more focused and have clarity. Clarity within the leader can bring up conditions of calm and peace that give a significant effect on how to respond to an existing condition. By being calm, then the leader has clarity and creativity. This situation helps the leader to recognize what is happening and helps further to decide what to do. This decision helps leadership in responding to the situation. Mindful leadership focuses on actions and internal circumstances, not on intellectual quality. The development of the leaders’ internal state is related to the behavior and emotional state of each of them. Leadership that provides democratic qualities in the delivery of opinions and decision-making processes both personally and collectively strengthen the effectiveness of his leadership, and develop essential behaviors related to emotional and social intelligence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".