Assessing the Sustainability and Performance of Local Soybean Production in Indonesia: A Multidimensional Scaling Analysis
Bibliographic record
Abstract
Local agricultural commodities in Indonesia require efforts to improve performance as well as soybean commodities.The prolonged deficit of local soybeans in meeting national soybean needs raises the suspicion that the imported soybean option is a more reliable deficit solution.This raises the fundamental question of how sustainable local soybean production will be in the future.The multidimensional scaling analysis method was used to assess each attribute on an ordinal scale based on sustainability criteria using the Rap+ application.The study was conducted in 6 provinces in Indonesia, which were determined deliberately by considering the level of productivity.The diagnosis results showed that the average sustainability index value ranged from 38.67-49.54.This shows that the sustainability status is in the less sustainable category.In the social dimension, the most sensitive attribute, namely the leverage attribute that if intervened will cause an increase in the sustainability status of the social dimension is agricultural extension.Research findings related to agricultural extension are suspected that the better the condition of agricultural extension services, the higher the average soybean production.Follow-up to the diagnosis results can be done by clustering measures.This is a rational action to achieve effective mutual development between provinces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".