Model Perencanaan Tata Ruang Partisipatif untuk Pengembangan Heritage Education Tourism di Situs Cagar Budaya Kota Kapur Kabupaten Bangka
Bibliographic record
Abstract
Situs Cagar Budaya Kota Kapur di Kabupaten Bangka memiliki nilai historis tinggi sebagai pusat peradaban Sriwijaya, namun pengelolaannya masih menghadapi masalah serius seperti alih fungsi lahan, infrastruktur terbatas, dan rendahnya partisipasi masyarakat. Penelitian ini bertujuan merumuskan model perencanaan tata ruang berbasis zonasi partisipatif untuk mendukung pengembangan heritage education tourism. Metode yang digunakan adalah kualitatif deskriptif melalui observasi, wawancara, diskusi kelompok, serta telaah dokumen regulasi dan literatur. Analisis SWOT dan pendekatan spasial digunakan untuk mengidentifikasi potensi, kendala, serta penyusunan zonasi sesuai prinsip pelestarian, edukasi, dan pemberdayaan masyarakat. Hasil penelitian menunjukkan bahwa delineasi empat zona yaitu zona inti, zona penyangga, zona pengembangan, dan zona penunjang dapat meningkatkan efisiensi pengelolaan ruang. Selain itu, integrasi UMKM lokal (lidi nipah, madu kelulut/pelawan, dan kerang darah) dalam zona penunjang membuka peluang pemberdayaan ekonomi yang memperkuat partisipasi masyarakat. Kebaruan (novelty) penelitian ini terletak pada pengembangan model tata ruang partisipatif yang menggabungkan konservasi sejarah, fungsi edukatif, dan ekonomi kreatif masyarakat di kawasan cagar budaya pulau kecil. Secara teoretis, penelitian ini memperkaya literatur heritage planning dengan pendekatan spasial-edukatif-partisipatif, sedangkan secara praktis memberikan rekomendasi kebijakan untuk mewujudkan pengelolaan cagar budaya yang inklusif, adaptif, dan berkelanjutan.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".