The Sustainable Development Status of Farming in Dam Service Area on the Indonesia-Timor Leste Border
Bibliographic record
Abstract
The research focuses on the objectives of ( 1) to analyze the sustainable development index of farming based on the development dimension in dam service area on the Indonesia-Timor Leste border, (2) to analyze the influence of economic, social and ecological factors on sustainable development farming in dam service area on the Indonesia-Timor Leste border.The research uses a survey method.Primary data were obtained from 300 farmers who were determined randomly.Secondary data were obtained from the Food Crops and Horticulture Department, Central Statistics Agency, BMKG and other agencies.Data analysis using RAP Farm and PLS.The results of the RAP Farm analysis found that the sustainability index of farming in dam service area on the border country was moderate sustainable.Then, the results of the PLS analysis also found that economic factors significantly, influence sustainability, while social and ecological factors do not significantly influence sustainability.Likewise, production and income factors have an impact on sustainability, while equity and reduction of greenhouse gas emissions are not sustainable.Therefore, agriculture policies in dam service area are needed that guarantee linkages between development dimensions so that sustainable economic growth, income distribution and reduced gas emissions can be achieved simultaneously; and to expand the study of impacts across time and across countries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".