Strengthening Religious Moderation: applying nine core values in Religious Moderation Village
Bibliographic record
Abstract
Religious moderation is a must in a nation that has diversity in various things, especially religion. To support this, the Ministry of Religious Affairs of the Republic of Indonesia launched the Religious Moderation Village program. This program is based on the application of nine values of religious moderation, namely tawasuth (moderation), i'tidal (upright and proportional), tasamukh (tolerance), deliberation, al-ishlakh (improvement), qudwah (leadership/pioneering), muwathonah (love for the country), al-a'naf (non-violence), and i'tiraf al-'urf (friendliness to culture). This study is qualitative descriptive, the research population is the community of Sidodadi village, Gedangan district. Data collection was conducted through in-depth interviews and observation, while data analysis used the Colaizzi approach. The results showed that of the nine values of religious moderation, four values have been implemented well in Sidodadi Village. The results showed that of the nine values of religious moderation, four values have been implemented well in Sidodadi Village, namely tasamukh (tolerance), tawasuth (moderation), muwathonah (love of the country), and i'tiraf al-'urf (friendliness to culture). The other five values, deliberation, al-a'naf (non-violence), i'tidal (upright and proportional), al-ishlakh (improvement), and qudwah (l leadership/pioneering), still face various obstacles in their application. This research provides an overview of the successes and challenges of implementing religious moderation values at the local community level.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".