The Village Fund Program and Indonesia’s 18th Sustainable Development Goal: A Bibliometric and Content Study
Bibliographic record
Abstract
The Village Fund (VF) is a programme initiated by the Indonesian government, aiming to fund activities that contribute toward achieving the Sustainable Development Goals (SDGs) in villages.Additionally, the Village Agenda-the 18th SDG-is a government initiative that employs local wisdom to implement the SDGs.This study aims to provide a detailed understanding of the VF and its implementation strategies to achieve the SDGs based on local wisdom.We began our work by compiling a literature review from the Scopus database, using a unique combination of a systematic literature review (SLR) and the PRISMA protocol.This process yielded 168 documents, published between 2014 and 2023, from the initial search and screening results.After further refining to ensure relevance, we were left with 151 articles that were analysed using the bibliometric method with VOSviewer, identifying nine clusters of research related to VF.The next step involved a content study of 115 documents, obtained from screening manuscripts using the keywords 'VF', 'sustainability', and 'Indonesia', to explore the characteristics of VF publications in the context of SDG implementation.To suggest strategies for how VF programmes can support SDGs in villages, we utilized a supplyside approach as well as a demand-side approach.Each approach has its advantages and disadvantages, depending on the economic conditions and socio-political environment.The results of this study will provide policymakers with insights into the critical factors influencing the effectiveness of VF distribution for achieving the SDGs.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.027 | 0.053 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".