Sustainability Index of Robusta Coffee Plantation (Case Study: Wagir District Smallholder Coffee Plantation in Malang, Indonesia)
Bibliographic record
Abstract
Smallholder coffee plantation are widely distributed in Indonesia, and it also support the development of this country coffee industries.To establish the best coffee development policy in Indonesia, the analysis of sustainability index is quite important.This research wants to analyze the sustainability index in one of the potential coffee plantations in Indonesia located in Wagir District, Malang Regency.The questionnaires were distributed to 20 farmers and conducted an interview to five expert that was selected based on purposive sampling method.To elaborate the sustainability index, Multidimensional Scaling analysis (RAP-Coffee) was used.There are four dimensions on this analysis, namely Ecology, Economy, Social Institutions, and Technology.Analysis results showed that the sustainability index for smallholder coffee plantation in Wagir District is 43.42 (less sustainable).From dimension analysis, it is showed that Ecology dimension has four sensitive attributes, Economy has three sensitive attributes, Social Institutions has five sensitive attributes, and Technology has five sensitive attributes.To improve the Indonesia smallholder coffee plantation and industries, the policy makers must pay more attention on these sensitive attributes.So, it can encourage the formulation of strategic and targeted policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".