Strategi Pengembangan Kebun Tanaman Anggur Dalam Lingkup Reforma Agraria di Kelurahan Duyu Kota Palu
Bibliographic record
Abstract
Desa Duyu merupakan sebuah desa di Kecamatan Tatanga Kota Palu yang telah ditetapkan sebagai pelaksanaan program reforma agraria tahun 2021. Tujuan dari penelitian ini adalah merumuskan strategi pengembangan strategi komoditas kebun anggur. Dimana strategi pengembangan dirumuskan, rencana pelaksanaannya dalam jangka menengah yaitu 10 (sepuluh) tahun. Serta mengidentifikasi tingkat perkembangan usaha kebun anggur di Desa Duyu berdasarkan persepsi pengunjung kebun anggur. Metode penelitian yang digunakan dalam penelitian ini adalah deskriptif kuantitatif. Dari hasil analisis SWOT telah dirumuskan 11 (sebelas) strategi pengembangan komoditas perkebunan anggur yang disesuaikan dengan hasil analisis yang diperoleh dari matriks IFAS dan matriks EFAS yaitu usaha pengembangan dalam keadaan hold and maintenance. Pengembangan komoditas kebun anggur di Kecamatan Duyu untuk kondisi saat ini masih dalam tahap bertahan dan berkembang. Artinya strategi pengembangan yang dirumuskan sejalan dengan level yang dimiliki. Tentunya realisasinya membutuhkan keterlibatan banyak pihak, mulai dari kelompok tani, instansi pemerintah, kelompok UMKM, swasta, dan/atau koperasi.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.023 | 0.004 |
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".