Pembelajaran Partisipatif Secara Daring bagi Petani Sorgum di Kabupaten Ende, Nusa Tenggara Timur
Bibliographic record
Abstract
Dryland is a potential food production in the future and sorghum is one of the potential commodities to be developed. Sorghum has been cultivated by a small number of people in Ende Regency, such as in Kotabaru and Nangapanda sub-districts. However, the cultivation has not yet applied cultivation techniques to achieve production optimization. In addition, there are still many limitations in knowledge of processing the production of sorghum seeds, leaves, and stems. Farmers have not enjoyed and received the benefits of cultivation so far. The purpose of this study is to increase awareness, understanding, and knowledge about the importance of sorghum to support food security and farmer welfare in Ende Regency, and to map the potential of online learning for farmers. The online learning was carried out in Kotabaru Village with the target being the Kema Sa Ate Women Farmers Group (KWT), which are sorghum cultivators. Learning is carried out using the lecture plus method (lecture-discussion) using a zoom meeting. Learning materials about harvesting and post-harvesting sorghum. The obstacle faced in this online learning is a device that does not support it. This problem was solved by involving a learning facilitator played by Field Agricultural Extension (PPL). Participants' initial knowledge before the training program was 3.8 and after training the final knowledge was 7.2. Based on the initial and final knowledge, it can be concluded that there is an increase in knowledge of 89.5%. The level of participant satisfaction with the 5 indicators proposed in the evaluation of the learning implementation process is very high, more than 80%. The level of participants' satisfaction with the five indicators in the evaluation of the training process also increased. More than 90% for problem solving in the field, speaker competence, and the level of urgency of information, while for media innovation and training methods more than 80%.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".