Factors Affecting Ethnic Harmony between Sinhalese and Muslim Communities in Post-war Sri Lanka: A Study Based on South Eastern University of Sri Lanka
Bibliographic record
Abstract
Sri Lanka is home to multi-cultural communities. It is the responsibility of the people across various religions, and communities to develop and maintain harmony with each other. Historically, the Sri Lankan Muslims and Sinhala Buddhists had an excellent relationship. Recently, the ethnic harmony between these two communities has been strained reflecting the fault lines running in a current social structure which lead to ethnic tensions, social animosities, restlessness, and disharmony among communities, amidst diverging political ideologies. Hence, this study focuses on identifying the root causes that wreck the harmony and social stability of the country. Hundred and fifty students from the South Eastern University of Sri Lanka were randomly selected to respond for a structured questionnaire, and fifteen formal interviews with students were also conducted to validate the questionnaire data. The secondary data were collected from various sources of information. The collected data were analyzed using descriptive and basic statistical analytic techniques, and findings of the study were presented in the form of table and text. This study underlines the array of reasons, and root causes that prevent the harmony among Sinhala Buddhists and Muslim communities, such as ethnic differences, spreading hatred via social media, extremism that uses religion to forward their extremist ideologies. This study concludes with the argument that the government and people who strive for social harmony should act with commitment and dedication in the efforts to build harmony among religious communities in post-war 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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".