An Analysis of Technical Efficiency of Rice Production in Indonesia
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
The objectives of this paper are to estimate technical efficiency in rice production and to assess the effect offarm-specific socio-economic factors on the technical efficiency using survey data from 15 provinces inIndonesia, collected in 2008. A stochastic frontier production function model is used to estimate the technicalefficiency of rice farms in each province, and using the model, the influence of socio-economic factors onefficiency is also measured. This study finds that there is a sizeable degree of variation of inefficiency betweenthe 15 provinces. It also finds that factors like land size, income and source of funding are influentialdeterminants of technical efficiency. In terms of age, it also found that younger farmers tend to be more efficient.Expanding the agricultural area, especially outside Java and Sumatera Islands, improving farmers’ income andgiving an incentive to young people to work in the agricultural sector will enhance technical efficiency and thusproductivity, as well as the overall rice output.
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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 | 0.015 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.036 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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