The Performance Enhancement Model for Coconut Processed Products Cooperatives through the Value Chain and Livelihood Assets Approach in North Maluku Province, Indonesia
Bibliographic record
Abstract
Objective: This research aims to formulate a model of value chain and livelihood assets in an effort to enhance the performance of cooperatives focusing on processed coconut products in the North Maluku Province. Coconut (Cocos nucifera) is a fruit plant with a crucial role on a global scale, providing a food source for millions of people, especially in tropical and subtropical regions. Due to the numerous benefitsit yields, the coconut is often referred to as the "tree of life" or the "rescuer tree Method: A total of 206 samples were used in this study, and the analysis was conducted using the Structural Equation Modeling (SEM) method. The determination of the research location is purposive, primarily in the largest coconut-producing areas in North Maluku. The selection of the research location is based on the consideration that this region is one of the largest coconut producers in Indonesia and has the presence of cooperatives in the coconut plantation sector. North Maluku Province produces coconut products in several regencies, including North Halmahera Regency, South Halmahera Regency, West Halmahera Regency, Central Halmahera Regency, Morotai Island Regency, Sula Regency, Taliabu Regency, and North Maluku Regency. Results: The research results indicate that primary activities, social capital, physical capital, entrepreneurship, and political capital have a positive and significant influence on the value chain and livelihood assets. On the other hand, support activities, human capital, natural capital, and financial capital do not have a significant impact on either the value chain or livelihood assets. The value chain and livelihood assets significantly contribute to the performance of the cooperative. Conclusions: factors such as support activities, human capital, natural capital, and financial capital do not have a significant influence on the value chain or livelihood assets. These results can serve as a foundation for policymakers and stakeholders to develop more effective strategies and programs to enhance the value chain, livelihood assets, and cooperative performance in the North Maluku Province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".