Pendampingan Diversifikasi Hasil Pertanian di Masa Pandemi: Strategi Ketahanan Pangan Masyarakat di Kawasan Urutsewu Kebumen
Bibliographic record
Abstract
Keberadaan Pandemi saat ini telah membuat ketahanan masyarakay menjadi menurun, bukan hanya terkait dengan kesehatan, namun juga diperparah dengan menurunnya distribusi hasil pertanian diakibatkan adanya pemberlakuan pembatasan berskala besar hampir di semua wilayah. Kondisi ini tentunya juga berdampak pada perekonomian masyarakat khususnya petani. Salah satunya yang paling terdampak adalah produk hasil pertanian, dikarenakan adanya pembatasan. Kawasan Urutsewu Kabupaten Kebumen adalah salah satu lokasi yang pada saat ini mengalami panen raya sehingga banyak hasil panen toidak dapat dipasarkan diantaranya adalah singkong, jagung, sayur mayur dan buah-buahan. Berdasarkan permasalahan tersebut maka kegiatan pengabdian ini berusaha membantu masyarakat didalam melakukan diversifikasi produk pertanian serta membantu memasarkan produk melalui marketplace. Potensi yang dimiliki oleh kawasan urutsewu adalah sudah terbentuk kelompok yang peduli terhadap potensi yang dimiliki. Kelompok Kadang Tani Milenial ini yang menjadi sasaran jangka pendek dalam membantu masyarakat melakukan diversifikasi hasil 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 | 0.001 |
| 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 | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".