ISLAM MENGHADAPI JUNTA MILITER DI MYANMAR
Bibliographic record
Abstract
Islam mengalami pasang surut perkembangannya di Myanmar. Pada abad ke-1 Pedagang Arab sudah menempati wilayah sekitar Arakan. Pelaut Muslim telah datang ke Myanmar pada abad ke-9. Orang Arab muslim telah berperan dalam pemerintahan di Myanmar. Raja Sawlu (1077-1088), dididik oleh seorang guru muslim berkebangsaan Arab. Negara Islam didirikan di Arakan ketika Sultan Bengal yang beragama Islam Naseeuruddeen Mahmud Syah (1442-1459, membantu Raja Sulayman Naramitha membangun Negara Islam. Muslim di Myanmar juga melakukan perlawanan terhadap tindakan kesewenang-wenangan yang dilakukan oleh Junta Militer. Salah satu bentuk perlawanan mereka adalah membentuk organisasi, salah satunya adalah Oganisasi Nasional Arakan Rohingya (ARNO). Organisasi ini merupakan gabungan dari Front Islam Rohingya (ARIF) yang dipimpin oleh Nurul Islam, Organisasi Solidaritas untuk Rohingya (RSO) yang dipimpin oleh Dr. Yunus dan RSO pimpinan Prof. Muhamma Zakaria. Tahun 1996 terjadi intensitas yang tinggi perlakuan sewenang-wenang dari pemerintahan militer Myanmar. Puncaknya pasca peristiwa 11 September 2001 di Amerika Serikat mengakibatkan tekanan terhadap muslim Myanmar bertambah keras. Tercatat sekitar 1.500.000 muslim Myanmar harus mengungsi ke Malaysia, Bangladesh, Thailand dan lain-lain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".