Problems Faced by Muslim Converts in Sri Lanka: A Study Based on Anuradhapura District
Bibliographic record
Abstract
Muslim converts are living with several problems after the conversion, and they are disowned and separated by their original relatives. Muslims by birth call Muslim converts as 'Moula-Islam' which is keeping off them as a different segment. The aim of this research is, therefore, to identify the problems faced by Muslim converts in Anuradhapura district, Sri Lanka. This is an empirical study with the applications of qualitative and quantitative data. The study adopted the questionnaire survey and in-depth interview techniques to collect primary data and randomly selected sixty-five samples out of three hundred sixty-five Muslim coverts living in Anuradhapura district. The significant finding of the study reveals that Muslim converts are facing several socioeconomic problems including the separation from family and relatives, the language problem, financial issues, the disparity in the aspect of marriage and the occurrence of divorces among married couples. The study further highlights difficulties faced by Muslim converts in terms of Islamic knowledge, learning Al-Quran, adopting Muslim cultural identity. Muslim converts are the most vulnerable people in the Muslim community, and they do not receive financial help, including Zakat from traditional Muslims. Hence, this study argues that current problems faced by Muslim converts should be addressed meaningfully and the Muslim community and voluntary organizations should take corrective measures to improve the life of Muslim converts in the Sri Lankan context.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".