Buddha Contemplation: Development of a Training Model to Improve Community Leader Virtue in the Central Region of Thailand
Bibliographic record
Abstract
This qualitative research aimed: a) to study the training model for developing the virtue of community leaders in central Thailand; b) to study the problems and suggestions of the training model for developing the virtue of community leaders in central Thailand; c) to develop the training model for developing virtue of community leaders in central Thailand. The research area consisted of three provinces in Central Thailand, Nakhon Nayok, Pathumthani and Phra Nakhon Si Ayutthaya. The research tools were preliminary survey, interview, observation, group discussion and workshop. Field data was collected from three groups of informants. The collected data was validated using a triangulation method and analyzed in accordance with the research objectives. The research results were presented as a descriptive analysis. The final results led to the setup of a new model of training, which can be called the ‘Buddha Contemplation: Development of a Training Model to Improve Community Leader Virtue’. The content of the course takes four Buddhist principles as its foundation: Sammaditthi (right views), Jarit (behavior), Sikkha (training) and Bhavana (development). The training method is on the principle of the kanlayanmit (seven suitable preparation conditions). The development of the training model for developing community leader virtue based on the Buddha Contemplation method can be implemented as a training technique for more effective development of community leader virtue.
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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 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".