Perbedaan Persepsi, Pendapatan dan Tingkat Risiko Usaha Tani Padi Sistem Tebas dan Non Tebas di Desa Sarimulyo, Kecamatan Winong, Kabupaten Pati
Bibliographic record
Abstract
Penelitian ini bertujuan untuk: (1) Mengetahui persepsi petani padi terhadap sistem tebas dan non tebas meliputi persepsi terhadap aspek ekonomi, aspek praktis, dan aspek risiko, aspek sosial.(2) Mengetahui perbedaan pendapatan usaha tani padi yang diterima petani menggunakan sistem tebas dengan non tebas.(3) Membandingkan risiko dan strategi mengatasi risiko usaha tani padi dengan sistem tebas dan non tebas.Penelitian dilaksanakan pada bulan Februari-Maret 2018.Jenis penelitian ini adalah penelitian deskriptif kuantitatif.Teknik penentuan sampel menggunakan kuota sampling.Responden penelitian ini adalah 30 petani sistem tebas dan 30 petani sistem non tebas.Pengumpulan data diperoleh menggunakan metode survei, observasi lapang dan wawancara terstruktur kepada petani menggunakan kuesioner.Hasil penelitian persepsi petani dalam memilih sistem penjualan padi dengan sistem non tebas lebih baik dibandingkan dengan sistem tebas.Tidak terdapat perbedaan pendapatan antara sistem penjualan tebas dan non tebas.Berdasarkan
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".