Peran Dinas Pertanian Dalam Pemberdayaan Petani Jagung di Kecamatan Tellusiattingnge Kabupaten Bone
Bibliographic record
Abstract
This study aims to determine the Role of the Department of Agriculture in Empowering Corn Farmers in Tellusiattingnge District, Bone Regency. The research method used is qualitative research and the number of informants is 12 people. This study uses data analysis techniques consisting of: data reduction, data presentation, and drawing conclusions. The results of this study indicate that the Bone District Agricultural Service has carried out empowerment by carrying out counseling and coaching programs, providing infrastructure and facilities, as well as monitoring and evaluation in accordance with the stages in the empowerment process according to Bone Regent Regulation Number 69 of 2017. For Tellusiattingnge District there are three the villages that are centers of corn cultivation according to the highest level of corn production in the Tellusiattingnge District are: Ulo Village, Pongka Village, and Palongki Village. Seeing the potential possessed in Tellusiattingnge Subdistrict for corn plantations, it is necessary to empower the government in a sustainable manner by conducting counseling and coaching, providing infrastructure and facilities, as well as monitoring and evaluation in collaboration with farming communities in order to create the lives of farming communities, especially superior and prosperous corn farmers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".