Quantifying the Effect of Religion on Rural Development in Indonesia Using the Rural Islamic Religiosity Index: A Case Study in West Sumatera Province
Bibliographic record
Abstract
The role of religion in development is crucial for implementing policies that aim to achieve sustainable development goals.This study seeks to quantify the role of religion in rural development.The research was conducted across 802 villages in West Sumatra Province, Indonesia, drawing on data from the 2018 Village Potential Statistics.Firstly, the study constructed a Rural Islamic Religiosity Index measurement, adopting the Alkire-Foster method.This newly developed index serves as a composite indicator of worship, education, economy, and social dimensions.The study then employed the Ordinary Least Squares (OLS) method for estimation.The assembled index serves as the independent variable while the Village Development Index, encompassing three dimensions, acts as the dependent variable.The results indicate that religion exerts a positive and significant influence on rural development broadly, particularly on economic and social aspects of rural development.However, the impact of religion on rural ecological development is subject to debate.The study recommends prioritizing spiritual development by enhancing religious understanding in integrated rural development activities, increasing the role of religious organizations in rural development planning, and building the capacity of these organizations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".