History, Local Wisdom “Ima Kokiriwo” Coconut Based Agroforestry and Land Use Policy in North Halmahera
Bibliographic record
Abstract
For the people of North Halmahera, coconuts represent a crucial agricultural commodity, yet information related to this topic remains relatively limited.This research aims to explore the history, level of adoptability, and the process of coconut processing, including the role of government policies related to land use.Phenomenological methods and snowball sampling were employed in the research, especially in data collection through interviews, observation, and focus group discussions (FGDs).The data were analyzed using the triangulation method, combined with literature studies, and the level of perception was measured using a Likert scale and quantitative analysis.The results revealed that coconut plantations were first independently cultivated by the Dutch in 1896, while Zending began cultivation between 1902 and 1910.The harvesting and processing of coconuts into copra adhered to the local wisdom principle "Ima Kokiriwo," which signifies working together in groups.The pattern of land use is predominantly (92%) mixed dryland farming, with an annual addition of land area of 3.3 hectares typically occurring in dryland agricultural cover types.The findings of this research support local government policies, particularly those related to the development of coconut cultivation based on traditional wisdom principles.Wise land use and sustainable agroforestry system programs have been effective in increasing land and coconut fruit productivity in North Halmahera, despite post-harvest processing not yet significantly augmenting household income.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".