Beyond Words, Beyond Conflicts: Narrative Inquiry into the Language Games of Asian Interreligious Dialogue
Bibliographic record
Abstract
Aim/Purpose This study aims to explore the dynamics of interreligious dialogue (IRD) among Buddhist, Hindu, Muslim, and Christian leaders in Sri Lanka and the Philippines using Ludwig Wittgenstein’s concepts of language games and rule-following. It seeks to: (1) analyze how religious traditions function as distinct yet overlapping language games; (2) identify barriers to effective IRD (e.g., political exploitation, historical grievances); and (3) develop context-sensitive strategies to foster interreligious harmony in pluralistic societies. Background Asia’s religious diversity presents opportunities for enrichment but also challenges due to linguistic, cultural, and doctrinal differences. Colonial legacies and nationalist projects have exacerbated tensions, transforming religious identities into political markers. Existing IRD models often overlook micro-linguistic barriers, assuming religious concepts are universally translatable. Wittgenstein’s philosophy offers a framework to address these gaps by emphasizing contextual meaning and communal practices over abstract definitions. Methodology The study employed a qualitative narrative inquiry design, engaging 18 religious leaders (10 from Sri Lanka and 8 from the Philippines) representing Buddhism, Hinduism, Islam, and Christianity. Data were collected through in-depth interviews, conducted both face-to-face and virtually, and analyzed using thematic coding with a Wittgensteinian framework. Ethical considerations included obtaining informed consent, ensuring confidentiality through coded identifiers, and conducting member checking to validate findings. Contribution This study makes four key contributions. Theoretically, it pioneers the application of Wittgenstein’s language games to Asian interreligious dialogue, showing how terms like dharma and jihad gain meaning through specific cultural contexts. Methodologically, it innovatively blends narrative inquiry with linguistic analysis to document real-world interfaith encounters. Practically, it offers concrete solutions for peacebuilding, such as creating hybrid terms like “peace guardians” that respect different traditions while fostering mutual understanding. It contributes to the Informing Science discipline by framing interreligious dialogue as an informing process, demonstrating that effective dialogue – like effective informing – requires linguistic humility and shared practices to bridge gaps across communities. Findings Common Ground: Shared ethical values (justice, peace) exist across traditions but are expressed through distinct language games. Misunderstandings: Decontextualized terms (e.g., jihad as “holy war”) fuel conflict; political manipulation and historical grievances persist. Future Aspirations: Leaders emphasize mutual respect, youth education, and structured dialogue to counter extremism. Recommendations for Practitioners Linguistic Mapping: Train facilitators to analyze religious terms within their native contexts. Hybrid Practices: Develop shared rituals (e.g., interfaith community projects) to foster organic understanding. Power Balancing: Ensure minority voices are included in dialogue structures. Recommendations for Researchers Expand studies to digital platforms and longitudinal designs. Compare IRD dynamics across Asian contexts (e.g., Sri Lanka vs. Indonesia). Include marginalized voices (women, youth) in future research. Impact on Society The study highlights how grassroots initiatives (e.g., Mindanao’s peace guardians) can decolonize IRD and build sustainable harmony. By reframing dialogue as shared practice rather than doctrinal debate, it offers pathways to reduce polarization in pluralistic societies. Future Research Explore AI and social media’s role in shaping interreligious dynamics. Investigate meta-language games for irreconcilable doctrinal differences. Examine the long-term efficacy of hybrid language games in conflict resolution.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".