LANDASAN DAKWAH MULTIKULTURAL: STUDI KASUS FATWA MUI TENTANG PENGHARAMAN PLURALISME AGAMA
Bibliographic record
Abstract
Studi ini berangkat dari gagasan dakwah multikultural yang relevan dengan konteks Indonesia. Sebagai sebuah pendekatan dakwah berbasis multikultural perlu memiliki landasan yang tepat dalam pelaksanaannya, karena konsep multikultural bersinggungan dengan konsep pluralisme teologis yang ternyata maknanya tidak tunggal. Sisi lain MUI sebagai Lembaga yang mengeluarkan fatwa Nomor 7 Tahun 2005 tentang keharaman pluralisme. Di sinilah perlunya pengkajian fatwa MUI tentang pengharaman pluralisme teologis sebagai landasan dakwah multikultural. Studi ini berfokus pada menelaah fatwa MUI tentang pengharaman pluralisme agama dan keabsahan makna khusus pluralisme agama, serta implikasi fatwa tersebut bagi pelaksanaan dakwah multikultural. Metodologi studi secara kualitatif deskriptif dengan pendekatan kepustakaan. Analisis berpijak pada konsep pluralisme, karakteristik dakwah multikultural, serta kelembagaan MUI dan fatwanya. Hasil studi menunjukkan bahwa pemaknaan pluralisme yang digagas MUI absah sebab berangkat dari pengertian awal pemahaman masyarakat, sehingga fatwa tersebut dapat diterima kebenarannya. Implikasi fatwa tersebut terletak pada tiga hal, yaitu perlunya pengembangan materi antipluralisme dan promultikultural, pengembangan strategi dakwah berbasis kultural dan mengedepankan kerukunan, penyikapan perbedaan dengan dialog dan toleransi aktif untuk hidup Bersama.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".