An Analysis of Technical Efficiency of Rice Production in Indonesia
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".