KESIAPAN MASYARAKAT DALAM MENGHADAPI PENGEMBANGAN EKONOMI KREATIF DI DESA TARAHAN, LAMPUNG SELATAN
Bibliographic record
Abstract
Pengembangan ekonomi kreatif di Desa Tarahan dilakukan dengan memanfaatkan beberapa potensi lokal seperti pertanian, perikanan, sumber daya alam, dan pariwisata Pantai Sebalang. Salah satu upaya nyata dalam mewujudkan ekonomi kreatif ini tercermin dalam Rencana Tata Ruang dan Wilayah (RTRW) Kabupaten Lampung Selatan untuk tahun 2011–2031. Namun, Desa Tarahan menghadapi tantangan sosial dan ekonomi, yaitu rendahnya tingkat pendidikan dan akses kesehatan yang terbatas. Kesiapan masyarakat adalah kondisi di mana mereka telah mempersiapkan diri untuk menghadapi perubahan dan peluang baru yang dibawa oleh pengembangan ekonomi kreatif (Tri-Ethnic Center for Prevention Research, 2014). Tanpa peningkatan kesiapan masyarakat, potensi ekonomi kreatif Desa Tarahan mungkin tidak dapat dimanfaatkan secara optimal, dan dampak positifnya terhadap pertumbuhan ekonomi masyarakat setempat bisa terhambat. Penelitian ini bertujuan untuk mengukur tingkat kesiapan masyarakat menggunakan dimensi Community Readiness Model berdasarkan teori Plested (2006). Penilaian kesiapan dilakukan berdasarkan pendapat responden masyarakat melalui penyebaran kuesioner. Responden ditentukan menggunakan teknik purposive sampling (usia produktif, yaitu di atas 15 tahun), dan pembagian sampel ke setiap strata jenis pekerjaan dilakukan dengan metode stratified proportional random sampling. Perhitungan skor mengikuti panduan dalam Community Readiness Model handbook. Hasil penelitian menunjukkan bahwa tingkat kesiapan masyarakat Desa Tarahan dalam menghadapi pengembangan ekonomi kreatif Desa Tarahan berapa pada tahap preplanning.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".