Drafting and Implementation of Village Spatial Plan (RTRDes): A Case Study in Indonesia
Bibliographic record
Abstract
The ongoing development exerting spatial pressure on the village area poses a threat to the survival of rural communities today.This development often treats the village as an object of exploitation, sparking tenure conflicts and contributing to natural disasters in the village.Village spatial planning is needed to bolster the village's position.Moreover, the territorial aspect of the village greatly contributes to the achievement of the Sustainable Development Goals (SDGs).While rules regarding village spatial planning are not yet established in Indonesia, several villages have initiated the preparation of Village Spatial Plans within their communities.The purpose of this study is to analyze the preparation and implementation of Village Spatial Plans in villages that have already undertaken this initiative.This research aims to illustrate the importance of a village having a spatial plan, hoping that the results can serve as material for policy formulation related to village spatial management in Indonesia.This research was conducted in 10 villages across 6 districts and 6 provinces in Indonesia.The study used a qualitative descriptive method with data collection through Focus Group Discussions (FGDs).The results indicated that community participation and mentoring support from external parties well-versed in spatial planning are crucial in the preparation and implementation of Village Spatial Plans.The existence of Village Spatial Plans provides economic, social, and environmental benefits.Apart from being a tool to clarify the identity of village potential, a Village Spatial Plan also serves as a mechanism to increase the value of that potential.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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, 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".