Intercultural Communication in the Temple: Challenges and Strategies for Thai Monks Using English to Share Buddhist Teachings
Bibliographic record
Abstract
This study examines the intercultural communication challenges and strategies employed by Thai Buddhist monks when engaging with international visitors in English. Guided by Byram’s Intercultural Communicative Competence (ICC) model, the research identifies cognitive, behavioral, affective, and developmental challenges faced by monks in conveying complex Buddhist teachings across linguistic and cultural boundaries. Utilizing a mixed-methods approach, 30 monks participated in surveys, and a subset of 10 monks engaged in semi-structured interviews. The findings reveal that cognitive challenges, particularly the translation of culturally specific Buddhist concepts, present significant obstacles. Behavioral and affective challenges, such as adapting non-verbal cues and managing emotional responses, further influence monks’ intercultural interactions. Additionally, developmental challenges highlight the gradual enhancement of intercultural competence through practice, reflection, and experience. To address these challenges, monks employ strategies closely aligned with Byram’s ICC model, including the use of simplified language, culturally relevant examples, empathy, and critical cultural accommodations. These strategies raise inclusivity, mutual respect, and effective intercultural dialogue. The study underscores the importance of intercultural competence training within religious contexts, advocating for skill development in adaptability, empathy, and culturally sensitive communication. By providing a detailed understanding of intercultural competence within religious education, this research highlights the dynamic and ongoing process of growth and adaptation in cross-cultural spiritual engagement.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".