UPAYA KELEMBAGAAN DALAM PENGEMBANGANPARIWISATA PESISIR DAN PULAU-PULAU KECIL YANG BERKELANJUTAN DI KABUPATEN BIMA
Bibliographic record
Abstract
Tujuan penelitian ini untuk mengetahui bagaimana upaya kelembagaan dalam pengembanganPariwisata peisisir dan Pulau-pulau kecil yang berkelanjutan di Kabupaten Bima.Jenis penelitian yang dipakai dalam penelitian ini ialah deskriptif kualitatif. Subyek penelitian ini dipilih melalui teknik purposivesampling.Teknik pengumpulan data terdiri atas observasi, wawancara, dan dokumentasi. Sedangkan teknik analisa data dilakukan melalui model interaktif Miles dan Huberman yaitu; reduksi data, penyajian data, dan penarikan kesimpulan/verifikasi. Hasil penelitian menunjukkan bahwa dalam upaya kelembagaan untukpengembangan Pariwisata peisisir dan Pulau-pulau kecil yang berkelanjutan di Kabupaten Bima dilakukan melalui upaya membangun kemitraan dengan komunitas kecil di Daerah, namun belum mampu menghadirkan dan menarik minat para investor untuk berinvestasi dalam menopang kepariwisataan karena masalah keamanan investasi dan lemahnya kepastian hukum. Selain itu pengembangan pariwisata pesisir terhambat kepentingan dan ego sektoral, dimana Peruntukkan dan jenis pengembangan kawasan Pesisir dan Pulau-Pulau kecil di Kabupaten Bima yang tidak berdasarkan Perda Tata Ruang Wilayah Kabupaten Bima dan Perda rencana zonasi dan Pengelolaan Wilayah Pesisir Dan Pulau-Pulau Kecil, sehingga seringkali mengintervensi keberlanjutan kawasan-kawasan yang harusnya di kembangkan menjadi destinasi wisata
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".