Cross Religious and Social Interaction: A Case Study of Muslims and Buddhists in Kampung Tendong, Pasir Mas, Kelantan
Bibliographic record
Abstract
The main objective of this study is to explore the quality of interaction between Muslims and Buddhists in Kampung Tendong. The researcher prepared one relevant indicator to measure quality interaction, that is religious understandings. Using a convenience sampling technique, a total of one hundred and forty (140) respondents were drawn from Muslims and Buddhists of Kampong Tendong, Pasir Mas, Kelantan. The sample size together with the above-mentioned indicators showed that the quality of interaction based on frequency is above average (i.e., 67.83%). Precisely, for “religious understandings” which were divided into two parts ‘intra-religious understandings’ was 82.95% and ‘inter-religious understandings’ was 34.34% with the average percentage of 42.59. The results show universal values of the two religions, namely Islam and Buddhism that bind people together. On the other hand, the discouraging factor of interaction among the residents of Kampung Tendong was that trivial issues of religious differences. Therefore, the significance of the study lies mainly in showing the level of interaction between Muslims and Buddhists in Kampung Tendong. This result is essential to the policy-makers to develop a better pattern of inter-racial interaction in a remote area of Peninsular Malaysia. The study finally discusses the need for broader and more comprehensive research in this area.
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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 | 0.002 | 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 | 0.013 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".