Quality of agricultural extension on productivity of farmers: Human capital perspective
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".