Bibliographic record
Abstract
This study aims to identify and analyze the potential of Buddhist communities in Central Java Province in order to strengthen religious identity and enhance their social engagement as a minority group within a multicultural society. The study employs a quantitative approach, collecting data from 376 respondents through a structured questionnaire. The collected data were analyzed using descriptive statistical techniques and Pearson correlation analysis to examine the relationships among the studied variables. The findings indicate that six key factors significantly contribute to strengthening religious identity and the spiritual resilience of Buddhist communities, namely cultural preservation, arts and traditions, utilization of cultural heritage sites, community economic development, and the implementation of religious activities. Among these factors, regular religious activities and the use of temples as religious tourism destinations emerged as the most dominant factors in encouraging community participation, social solidarity, and the sustainability of Buddhist teachings. These findings underscore the importance of adopting a holistic and locally grounded approach in community development initiatives. This study offers important implications for the formulation of religious policies and the development of more inclusive, contextual, adaptive, and sustainable strategies for the empowerment of Buddhist communities at both regional and national levels.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".