Analisis Minat Menjadi Petani dan Pemahaman Ilmu Pertanian di Kalangan Pelajar dan Mahasiswa di Kabupaten Semarang
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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