Technical Efficiency of Agricultural Farms in Khulna, Bangladesh: Stochastic Frontier Approach
Why this work is in the frame
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Bibliographic record
Abstract
This paper uses the stochastic frontier approach to measure technical efficiency level of the agricultural farms of Khulna, Bangladesh. It considers three sub-sectors: rice cultivation, fish cultivation and livestock rearing. About 76%, 81%, and 73% variations in output are due to technical inefficiency for the farms of the three sub-sectors, respectively. The highest variation in output (due to inefficiency) is found in the fish cultivation sub-sector. The sample farms are operating at an inefficient level and the inefficiency level decreases over time for the sub-sectors. The farming experience of the farmers and the availability of the credits significantly and positively affect the efficiency level of the farms. This study finds the necessity of redefining and redesigning the credit instrument for maintaining sustainability in the long run. It is also found that all the three sub-sectors have a chance to increase their production level with the same set of technology.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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