FAKTOR-FAKTOR YANG BERPENGARUH TERHADAP EFEKTIVITAS DISTRIBUSI PUPUK BERSUBSIDI DI PERBATASAN INDONESIA-RDTL (Studi Kasus Desa Ponu)
Bibliographic record
Abstract
Pupuk bersubsidi adalah barang yang diadakan oleh pemerintah kepada masyarakat guna untuk mempertahankan ketahanan pangan. Tujuan penelitian ini untuk menganalisis faktor dan tingkat keefektivitas distribusi pupuk bersubsidi di Desa Ponu. Waktu penelitian dari bulan Agustus-september 2021 di Desa Ponu Kecamatan Biboki Anleu Kabupaten Timor Tengah Utara. Populasi sebanyak 300 dan pengambilan sampel menggunakan Quota Sampling sebanyak 90 responden. Teknik pengumpulan data dalam penelitian ini berupa data primer dan data sekunder. Analisis dalam penelitian menggunakan Structur Equation Modeling Partial Least Square (SEM-PLS) dan deskriptif kuantitatif dengan menggunakan skala likert. Hasil analisis menunjukan bahwa variabel modal manusia, modal sosial, modal fisik serta kinerja penyuluh berpengaruh signifikan terhadap efektivitas distribusi. Efektivitas distribusi pupuk bersubsidi dilihat dari indikator 6 Tepat yang menjadi kriteria menunjukan bahwa indikator tepat harga, tepat tempat, tepat mutu efektif dan indikator tepat jenis, tepat jumlah, tepat waktu cukup efektif.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".