Bibliographic record
Abstract
Indonesia memiliki tantangan dalam mempertahankan lahan pertanian dan mencukupi kebutuhan pangan masyarakatnya di tengah tekanan urbanisasi. Pertambahan jumlah penduduk yang cepat mendorong kebutuhan permukiman dan fasilitas lainnya juga bertambah. Pertanian merupakan sektor yang paling rentan beralih fungsi disaat menghadapi perkembangan wilayah. Nilai ekonomi pertanian yang rendah dan berisiko tinggi merupakan salah satu penyebabnya dan umumnya kalah bersaing dengan sektor lainnya. Nilai ekonomi tanaman padi lebih rendah dibandingkan tanaman pertanian lainnya, namun menjadi kebutuhan pertanian utama di Indonesia yang masyarakatnya sebagian besar mengkonsumsi beras. Petani padi memerlukan dukungan berbagai pihak untuk tetap menjalankan aktivitasnya di sektor pertanian. Peran kebijakan menjadi sentral dalam melindungi dan memastikan keberlanjutan pertanian padi. Rekomendasi yang perlu dilakukan antara lain: peningkatan kesejahteraan petani melalui berbagai skema (tidak hanya insentif), peningkatan koordinasi antar pihak dari pusat sampai lokal, pelibatan pemerintah desa dalam program atau kebijakan pertanian, dan meningkatkan fasilitas-pelayanan di sekitar wilayah pertanian.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.013 |
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".