Determinants Transfer of Primary Business of Rice Farmers Household at Musi Rawas District South Sumatera Indonesia
Bibliographic record
Abstract
Indonesia is a country whose majority lives of agriculture and food crop agriculture remains the livelihoods of the majority of the Indonesian population. South Sumatera province is one that is a center for food crops, especially rice. A district that has irrigation and a rice production center in South Sumatera is Musi Rawas District. In 10 years (1993-2013) recorded a decrease in the number of rice farmers households is significant in Indonesia, including in South Sumatera. Changes in the amount of rice farming households in the province of South Sumatera by Agricultural Census 2013 indicates the state of declining, even in the central areas of food. This situation is further interesting to study the determinants of primary business of rice farmers to plant non-food and non-agriculture, especially in the central areas of food and irrigated in South Sumatera, Indonesia. This study used survey method and logistic regression for the analysis data. The result shows that factors affecting farmers’ decision to switch or not switch from the main businesses, namely rice farm to farm fish, rubber and non-agricultural businesses is land area, household income from rice, the income of non rice, grain price at farmers level, revenue from non paddy, costs of farming, commodity prices, employment opportunities outside of the main business, farming experience and knowledge of farmers on land conversion rules.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".