Arguing Islamophobia during COVID-19 Outbreaks: A Consideration Using Khuṣūṣ Al-Balwᾱ
Bibliographic record
Abstract
The threat of Islamophobia continues to surface. The latest is related to COVID-19. Islam considered as the source of the virus suddenly went viral, even with the hashtag #coronajihad. The implementation of religious rituals by ignoring social distance by certain groups can be one of the triggers besides propaganda and conspiracy from anti-Islam. This article aims to provide an argument against Islamophobia with consideration of Khusus Al Balwa. The approach used is a combination of normative and empirical facts amid the heterogeneity of Muslims during the pandemic. An interesting finding from this research shows that khusus al-balwa is a concept that Muslims need amid co-19 hegemony, especially in terms of providing a complex understanding to present a calming Islam rather than a threat. In reality, khusus al-balwa happened a lot amid the pluralism of Muslims to prevent the outbreak of Islamophobia amid co-19 issues. Consideration of khusus al balwa contribute to prevent the negative stigma that could harass verbally and physically to the muslem. In fact, the special concept of al-balwa has not been much studied by observers of Islamic law which is covered because of the 'fame' of ‘umum al-balwa. Khusus al-balwa has not been fully taken into consideration by the Mufti, both individuals, and institutions in bringing forth fatwa products.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| 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".