Foresight Strategy for Sustainable Oil Palm Development in East Halmahera Indonesia
Bibliographic record
Abstract
The huge contribution of oil palm to the Indonesia's economy and sustainability has been widely discussed and required further study.Nowadays, Indonesia's government is focusing on oil palm development in the east of Indonesia.One of the development sites is East Halmahera.However, the previous relevant studies have not investigated the oil palm development in the east of Indonesia.Therefore, strategy for sustainable oil palm development in East Halmahera is a novel and urgently needed for agricultural development programs in Indonesia.This study aimed to map the position of oil palm as an initiative commodity for development and formulate strategy and policy for sustainable oil palm development in East Halmahera.Sustainable development goals (SDGs) became basis for evaluation criteria of this study.Data were gathered through focused group discussion involving some representatives of key stakeholders such as local community, government and company.Preference Ranking Organization Methods for Enrichment Evaluation (PROMETHEE) and Multi-criteria Policy (MULTIPOL) were applied as data analysis with multi-criteria and prospective approaches.This study found that oil palm is a strategic commodity for regional economic development compared to mining.Furthermore, economic growth, inclusiveness and environmental preservation are foresight policy scenario for sustainable oil palm development in East Halmahera.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".