Quality of agricultural extension on productivity of farmers: Human capital perspective
Why this work is in the frame
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Bibliographic record
Abstract
The relationship between agricultural extension and farmer productivity has been widely discussed; agricultural extension directly or indirectly affects farmer productivity. In this study, the researchers attempted to elaborate on this matter by looking at it from the rural wing based on the human resource and human capital theory. This study uses a quantitative explanatory approach. The analytical tool used is Structural Equation Modeling (SEM) as a fundamental data analysis using AMOS software. The population in this study were all agricultural extensions in South Sulawesi and West Sulawesi. The sample was taken using the accidental sampling technique; it included only the completed questionnaire in the data analysis. Until the time limit, only 122 agricultural extension people filled out the questionnaire and were declared complete. Research shows that rural extension has a significant positive effect on soft-skill competence and not substantial on farmer productivity. Furthermore, soft-skill competence significantly affects farmer productivity and is a good mediator in increasing farmer productivity. The results show that it could improve farmers' productivity, not because of direct extension but because the farmers' soft competence increased due to interventions from the quality of agricultural extension workers. Therefore, good quality agricultural extension agents will encourage the rural farmers' ability to solve problems. Make systematic planning and communication skills that will help them build relationships with colleagues and stakeholders, improve ethics, discipline, increase their skills and experience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it