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Record W4388068296 · doi:10.18280/ijsdp.181018

Quantifying the Effect of Religion on Rural Development in Indonesia Using the Rural Islamic Religiosity Index: A Case Study in West Sumatera Province

2023· article· en· W4388068296 on OpenAlexvenueno aff
Muhammad Irfan, Irfan Syauqi Beik, Bambang Juanda, Sri Mulatsih

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsReligiosityIslamIndex (typography)Rural developmentSocioeconomicsGeographyReligious studiesPolitical scienceSociologyPsychologySocial psychologyPhilosophyAgricultureArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.333
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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