An Analysis of Technical Efficiency Variation in Indonesian Rice Farming
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rice farming in Indonesia has an important role as a sector producing staple food for almost all of the population and provides a livelihood for millions of people in rural areas. Conditions of rice farming in Indonesia are quite unique because it is scattered in many island with diversity of social and economic characteristics of farmers, environmental conditions, and potential production. This study apply two-stage Data Envelopment Analysis (DEA) to estimate technical efficiency and analyses the determinants of technical efficiency rice farming based on farm level data collected by the Central Bureau of Statistics the Republic of Indonesia. The results showed that the average technical efficiency in all the rice-producing regions in Indonesia is moderate to High. This study suggest that policy to increase the technical efficiency in Indonesian rice farming should be prioritized on the use of certified seeds, control of pests and diseases, government assistance, education and irrigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.030 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it