ANALISIS EFISIENSI TEKNIS USAHATANI PADI DI JAWA DAN LUAR JAWA : PENDEKATAN DATA ENVELOPMENT ANALYSIS (DEA)
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
Efficiency is an important aspect for farmer that can be used as a measuring tools to make a decision regarding production among available options. The objectives of this research is to evaluate the technical efficiency of paddy farming, and to identify the factors that influence the technical efficiency of paddyfarms in Jawa and outside Jawa. To analyzed the data, data envelopment analysis (DEA) approach and tobit regression were applied. Farmers were not use the the right amount of inputs as being recommended by the instructor, such as the use of seed, fertilizer NPK and urea. The use of Urea, NPK, and labor had the largest percentage of input slacks when compared to the other production inputs. Farmers in Jawa could reduce the use of urea by 6.75 kg, NPK by 14.96 kg, and labor by 7.45 HOK and farmers in outside Jawa could reduce the use of urea by 32.37 kg, NPK by 6.01 kg, and labor by 15.93 HOK to make the paddy farm technically efficient. One of the factors that can greatly influence the improvement of farming technical efficiency is the socio-economic factors. Factors that affecting the technical efficiency of paddy farm in Jawa were the age, the level of formal education, member of Farming group and the number of members in the household, and do not significantly affect the technical efficiency of paddy farm is acces formal finance, and ectention. Factors that affecting the technical efficiency of paddy farm in outside Jawa were the age, the level of formal education, and member of Farming group and, and do not significantly affect the technical efficiency is the number of members in the household, acces formal finance, and ectention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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 it