Effect Relationships on Sustainable Development of Palm Oil Production for Independent Smallholder Farmers toward Sustainable Certification System
Bibliographic record
Abstract
Palm oil is currently the most widely used vegetable oil in the world and its usage is also expected to double by 2020. However, there are some social and environmental impacts of palm oil plantation. Some complications resulted from the plantation may go as far as mass objections to the production of palm oil. On the contrary, demand for palm oil is still vast and constantly rising. In Indonesia, independent small farmers are the most important stakeholders since they are 43% of the whole Indonesian palm oil producers and have become the biggest spotlight of Indonesian palm oil development, including challenges and problems in which they will have to face to substantially increase their role in the global market as well as maintaining sustainability. Challenges today need to be engaged with innovation and inventions in a more productive and effective way. Enhancing independent small farmers will not only enlarge their contribution to sustainability practices, but also ensuring the sustainable products supplied to the market. Thus, supporting sustainable palm oil production is the way forward. Based on this current issue, this research identifies key point relationships (direct and indirect) on sustainable development factors which are based on Indonesian Sustainable Palm Oil Certification System (ISPO), these identified key points will be the primary target to be improved and government support in fostering the sustainability of palm oil industry will be profoundly necessary.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.001 |
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".