Tinjauan Kritis Proses Penyusunan Rencana Tata Ruang Desa: Kasus Desa Jepitu, Kabupaten Gunung Kidul, Provinsi Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
Spatial planning is an important policy in negotiating between increasing space requirements and existing space constraints. In the context of spatial planning at the village level, The Village Spatial Planning (VSP) can be one of solutions in solving spatial planning challenges in the scope of the village area. The existence of VSP has its pros and cons, due to the lack of empirical evidence on how village spatial planning should be done. This research aims to explain the background and process of preparing the VSP, as well as the factors that influence it based on inductive-qualitative approach. The research location was conducted in Jepitu Village, Gunung Kidul Regency, Yogyakarta Special Region Province. The consideration for choosing this location was due to various village management challenges that have the potential to impact space utilization, namely (1) Increasing the development of coastal tourism; (2) Southern Cross Road Program (SCRP); and (3) Village boundary issues related to the management of water resources. The data used are field observation data and in-depth interviews, and are supported by secondary data. The result of this research, there are internal factors and external factors that trigger the background for the preparation of the VSP. The internal factors consist of (1) Concerns of the Village Pamong; (2) Beach Tourism Area Management; (3) Economics; (4) Development Planning; and (5) Regional Mapping. The triggers from external factors, namely the presence of Supporting Agent. Meanwhile, the factors in the process of preparing the VSP, namely (1) Actors; (2) Community Participation; (3) Regional Mapping; and (5) Resistance. Based on the results of this study, VSP can be one of solution in solving various village area management challenges related to the provision spatial data on village boundaries and potentials to be used as a guide for village development planning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".