Intolerance in the Fatwa on the Prohibition of Interfaith Greetings: Its Impact on Islamic Family Law and Social Harmony
Bibliographic record
Abstract
The fatwa on the prohibition of inter-religious greetings issued by the Majelis Ulama Indonesia (MUI) in English Indonesian Theologian Council has caused controversy in the context of inter-religious relations in Indonesia. This study aims to evaluate the impact of the fatwa on Islamic family law and social harmony, focusing on the intolerance caused by interfaith interactions. Using a qualitative approach, this study conducted a content analysis of the text of the Indonesian Theologian Council fatwa and literature related to maqashid al-syariah and Islamic family law. Data were obtained through a documentation study of Indonesian Theologian Council fatwas, books, articles, and academic publications. The analysis technique involved identifying the main themes in the fatwa, comparing them with maqashid al-syariah principles, and evaluating their impact on multicultural family relationships and social harmony. The results show that this fatwa, although aimed at protecting Islamic identity, has the potential to cause tension in family relationships that have members with different religious backgrounds and undermine social harmony. The research emphasizes the need for open dialogue between scholars, academics and the community to find a more inclusive and tolerant solution. A more moderate approach is hoped to be adopted to create interfaith harmony and maintain harmony in Indonesia's multicultural society.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".