The Capability of the Meranti Islands Regency Government in the Development of Sago Based on Local Wisdom
Bibliographic record
Abstract
Various government initiatives to support food security based on local wisdom, such as planting sago, must be implemented through multiple agricultural potentials. On the other hand, the facts show that sago is still not consumed as a staple food and requires extensive local government expertise. Therefore, this study aims to examine the ability of local governments to produce sago based on local wisdom in the Meranti Islands Regency, Riau Province. This study uses a qualitative approach with data analysis techniques on the Nvivo 12 Plus software concept map analysis. Based on the research results, the ability of the Meranti Islands Regency Government has four dimensions of capability, namely: First, in the knowledge and skills dimension, the government has carried out the task with regional agencies in socializing the "One Day with Sago" program. Second, on the technical system dimension, the government already has a technical design and specifications for sago processing, focusing on developing small and medium-sized industrial centers. Third, in the Managerial System Dimension, the government has encouraged community empowerment and designated Meranti as a sago cluster area. Fourth, in the Dimension of Values, the government has made efforts to empower the community sustainably and has produced various innovations in the content of sago production.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".