Analisis Minat Menjadi Petani dan Pemahaman Ilmu Pertanian di Kalangan Pelajar dan Mahasiswa di Kabupaten Semarang
Bibliographic record
Abstract
The interest of young people in Indonesia to become farmers is decreasing, which can be seen from the decreasing percentage of young farmers. The purpose of this study is to see the extent to which students have interest and desire to play a role in agricultural development in Semarang Regency, by looking at the leverage factors so that the right approach can be taken in the preparation of the next agricultural development strategy. The approach taken in this study is a qualitative deductive approach with a purposive sampling data collection method with 248 people as respondents. The results of this study explain that there are 67.74% of respondents who are interested in becoming farmers with certain prerequisites that support their interests. Unfortunately, the respondents' understanding of agricultural science is still limited, where 43.55% of respondents understand agricultural science moderately, and only 7.66% understand agriculture in a broad sense. The most needed strategies to support the implementation of the respondents' interest are by strengthening and utilizing the latest agricultural technology, strengthening technical skills and modern agricultural knowledge for farmers, and increasing farmers' income through processing businesses, as well as developing agricultural knowledge and innovation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".