Technology gap ratio decomposition in smallholder solar saltworks in Indonesia using meta-frontier data envelopment analysis (MetaDEA)
Bibliographic record
Abstract
The increasing population in Indonesia results in the rising demand for consumer goods, including salt. Meanwhile, salt production in Indonesia remains traditional, using direct methods by evaporating seawater in open ponds near the coast, producing a final product called “solar salt”. This process depends on sunlight, air, weather, and seasonal climate conditions. This research aims to analyze the technical, technological, and managerial disparities among traditional solar salt farming businesses operated by local communities across regions in Madura Island—the foremost solar salt-producing region in Indonesia. This study employs primary data collected through surveys conducted during the production season in 2023/2024 in three regencies in Madura: Pamekasan, Sampang, and Sumenep. The structured questionnaires captured the input and output data. Meta-frontier Data Envelopment Analysis (meta-DEA) was applied to assess the technical efficiency of conventional solar salt productions across the research regions. The efficiency analysis revealed that, with the current production methods, solar salt farmers achieved an efficiency rate of 46.98%, with an average technical efficiency of 80.83%. This result shows that the decision-making units (DMUs) can enhance their technical efficiency by 19.07%. Meanwhile, the technology gap ratio (TGR) analysis indicates that Sumenep Regency has the highest TGR value, nearing the threshold of 1, suggesting a relatively low technology gap in this regency. The meta-DEA decomposition indicates that the determinant of the average meta-technical inefficiency among solar salt farmers is the technological disparities, with average technological gap inefficiency (TGI) values surpassing managerial gap inefficiency (MGI) values. Conversely, Sumenep Regency has a larger MGI than TGI value, implying that solar salt farmers in Sumenep possess lower managerial decision-making skills than in other regions. The findings suggest the need to enhance the adoption of the latest production technology innovations to address technological gaps in the research locations.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".