Analisis Jaringan Sosial Pariwisata di Kampung Pesisir Bulak Surabaya
Bibliographic record
Abstract
Berdasarkan RTRW Kota Surabaya tahun 2014-2034 dan RZWP3K Tahun 2012-2032, Kecamatan Bulak meliputi Kelurahan Kenjeran dan Kelurahan Bulak di kawasan kaki Suramadu diarahkan dengan fungsi utama sebagai kawasan wisata bahari/laut, pengembangan pariwisata alam dan buatan, dan permukiman nelayan. Adanya pembangunan fisik kawasan Pariwisata di Kampung Pesisir Bulak dan sekitarnya masih memyebebkan masalah non-fisik yang ditimbulkan dari adanya hubungan atau peran aktor dalam pengembangan kawasan pariwisata yang belum optimal. Untuk itu diperlukan pengembangan berbasiskan Jaringan Sosial dalam melihat hubungan dan kesinambungan antar peran dalam menyelesaikan masalah terkait pengembangan wisata di Kampung pesisir Bulak dan sekitarnya. Penelitian ini menggunakan Social Network Analysis (SNA) dalam menganalisis hubungan yang terjadi antar pihak yang berpengaruh dari kelompok pemerintah maupun kelompok organisasi masyarakat. Dalam melakukan Social Network Analysis (SNA) mengacu pada perhitungan Degree of Centrality, Closeness Centrality dan Betweenness Centrality dari setiap aktor yang teridentifikasi . Kemudian dari hasil analisis menunjukkan bahwa Dinas Pertanian bidang Perikanan dan Badan Perencana Pembangunan Kota Surabaya merupakan pihak yang memiliki nilai centrality tertinggi sehingga memiliki peran yang besar dalam pengembangan pariwisata kampug pesisir Bulak Surabaya.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".