New Holistic Strategy of Sustainable Rural Development Management-Experience from Indonesia: A PESTEL-SOAR Analysis
Bibliographic record
Abstract
This article offers a new strategy of holistic rural development by utilizing the external strengths of the rural and the internal strength based on the experience of one of the rural in Indonesia that has been succeeded in turning the rural from the poorest into the best in the national rank. The successful formula is associated with the role of village leaders in benefiting opportunities from the existing external-internal aspects. To capture more holistic development phenomena including political, economic, social, technological, environmental and legal phenomena while generating new bottom-up strategies, the study uses PESTEL and SOAR analysis. This study found that the first condition for rural development in Indonesia is the development of village leadership management strength in holistically managing the potential and opportunities of external and internal villages. It changes the fundamental paradigm that holistic rural development must be seen as a whole (the village can take advantage of the existing external-internal strengths) partially (the village only focuses on utilizing the village's internal strength utilization agricultural potential). Through the PESTELs-SOAR analysis approach, the strategy offered becomes more rational and comprehensive in sustainable rural development by collaborating the village bottom-up strategy approach while still considering prevailing external conditions (more top-down).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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, 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".