Mapping the landscape of institutional village-owned enterprises: A bibliometric analysis of literature from 2005 to 2024
Bibliographic record
Abstract
This study explores the institutionalization of village-owned enterprises (VOEs) in a program developed in Indonesia to encourage village economic independence through the management of local potential by the community. The study of VOEs institutions in recent years has experienced rapid growth, meaning that there is an increasing urgency to face this challenge. This data is based on a bibliometric analysis to evaluate the scientific landscape of village-owned enterprises using the Biblioshiny analysis tool in R-Studio, as well as VOSviewer and MS Excel. This study analyzes 197 articles published from 2005 to 2024, based on Scopus data, by applying inclusion and exclusion criteria through range, subject area, and document type. The main findings highlight trends in scientific work production, thematic analysis, most cited articles, country contributions, word cloud analysis, trend topics, most frequent words, and co-occurrence networks. The results of the analysis show that from 2005 to 2015, contributions were dominated by authors from Canada and the United States, but from 2016 to 2024, they were dominated by authors from Indonesia. The highest number of citations, namely 925, was published in 2006. The most frequently appearing words include community-based enterprise, sustainability, bumdes, and village-owned enterprises. Thus, VOEs have become one of the biggest challenges in managing economic potential, village assets, and public services in order to improve the welfare of villagers. The role of the government in realizing village independence is very important through the management of VOEs because basically the community will prosper if village income increases. These findings contribute to future research and practice as a reference for stakeholders in making policies, developing governance, research and practitioners in realizing regional economic independence that synergizes with government programs.
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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 | Not applicable | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.063 | 0.292 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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, 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".