Which One is Stronger to Affect Innovation Adoption by Balinese Farmers: Government Role or Local Wisdom?
Bibliographic record
Abstract
Bali is a popular tourist destination that still maintain its typical culture in many areas of life, including in agriculture. Basic implementation of the primary sector in this island is based on the local wisdom called Tri Hita Karana or three causes of happiness. Tri Hita Karana consists of Parahyangan, Pawongan, and Palemahan, the harmonious relationship between human and God, fellow human beings, and the environment. Decision-making of farmers to do adoption of innovations always considering compliance with the local wisdom. Agricultural innovation has been developed from the results of research and development by the government. The government has several functions in the agriculture sector, such as: regulation functions, education functions, control functions, supervise functions, and stabilization functions. This study aimed to analyze the effect of the implementation of local knowledge and the role of the government towards the adoption of innovation, and to determine the factors which have a dominant effect on the adoption of modernization. The results showed that both the implementation of local wisdom and government role have positive and significant effect on innovation adoption by Balinese farmers. In fact, the implementation of local wisdom is stronger to affect innovation adoption than government role. Therefore, it is suggested that in the research and development innovation for agriculture, the government and researcher always consider the suitability with local wisdom, so that innovations can be adopted by farmers optimally.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".