Muslim Socio-culture and Majority-Minority Relations in recent Sri Lanka
Bibliographic record
Abstract
Sri Lanka is a nation in which multi-religious, multi-ethnic multi-language people live. Buddhists are the majority, while Muslims form the second minority group next to Tamils. Since historical times, the community relationship between Buddhists and Muslims has been prevailing. However, recently, a disturbing trend has been widely spreading among the Buddhists and Muslims. This situation has emerged during the aftermath of the anti-Muslim campaigns set by a number of Buddhist Nationalist Groups (BNGs), with their main goal being to propagate incorrect opinions about the Muslims to promote negative views about their socio-culture, and to distort the idea of a peaceful relationship between Buddhists and Muslims in the country. Accordingly, in the past several years, they have campaigned against halal certification on consumer goods, hijab and niqab of Muslim women, cattle slaughtering, places of worship and prayer services, among others. Moreover, they spread out the illusion that the above aspects of Muslim socio-culture are notable threats to the Buddhist people. So, these aspects are assumed by the Buddhists to be obstacles for maintaining a community relationship with Muslims. On the above background, analyzing the extent to which the above aspects influence the majority-Buddhists and minority-Muslims relationship, and determining as to whether an unfastened relationship will prevail between them, are the main objectives of this study. Based on the results, it is certainly affirmed that the above Muslim socio-cultural aspects, except slaughtering of cattle, have not pushed their influences to damage the Buddhist-Muslim relationship in Sri Lanka. In this respect, it was found that the aspect of ‘slaughtering of cattle’ is the only obstacle to the Buddhist-Muslim relationship. Furthermore, the recent campaigns have not changed the Buddhists’ mood in terms of maintaining a better relationship with Muslims. Moreover, the campaigns did not change their habits in keeping up the relationships with Muslims, without any break as how they behaved during the war (1984-2009) and pre-war periods. However, it is worthy to note that the BNGs have succeeded through their campaigns to create a negative Muslim stereotype among a small population of Buddhists in Sri Lanka.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".