Deauthorization of the Religious Leader Role in Countering Covid-19: Perceptions and Responses of Muslim Societies on the Ulama’s Policies in Indonesia
Bibliographic record
Abstract
Ulama as epicenters of religious leader in spreading religious teachings in Muslim societies have experienced deauthorization. This study aims to reflect the Muslim societies’ perceptions and responses on the Ulama policies in countering Covid-19 in Indonesia. This study uses observation, interviews, and literature review as data sources. This study presents a theoretical perspective on the deauthorization of the Ulama role in Indonesia in handling Covid-19. This study found three socioreligious aspects. Firstly, less public’s knowledge and understanding of Covid-19 socialization confirms multiple interpretations at the grass road level. Secondly, the socialization of the Ulama policies has not been carried out effectively, as seen in several cases, such as the rejection of the mosque closure and the prohibition of other religious activities, due to the lack of public knowledge about this epidemic. Thirdly, government policies on Large-Scale Social Restrictions (PSBB) which are totally supported by the MUI (Indonesian Ulama Council) as the representation of Indonesian Muslim scholar have not been able to suppress the Muslim societies’ enthusiasm in the practice of religious activities. It indeed demonstrates Ulama’s deauthorization in countering Covid-19 pandemic for Muslim societies in Indonesia.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".