NETWORK GOVERNANCE DALAM PENGEMBANGAN SISTEM \nPERTANIAN ORGANIK DI KOTA BATU \n(Studi Pada Dinas pertanian dan Kehutanan Kota Batu)
Bibliographic record
Abstract
This study aims to describe network governance in the development of organic farming systems in Batu City. This research focuses on two things, such as describing the application of network-based cooperation implemented between the government, the private sector, and the community and the inhibiting factors of the collaborative process implemented in the development of organic farming systems in Batu City. Meanwhile, in order to answer these problems, this study uses the concept of network governance which describes several indicators in implementing government networks that include relations between actors, support of budgetary resources from the government and the private sector; coordination between actors; objectives and benefits; and evaluation monitoring process. \nThis study uses descriptive-qualitative research methods. The data sources are interview data and documents from the Batu City Agriculture and Forestry Service. While the research subjects were informants, namely employees of the Agriculture and Forestry Office of Batu City and Farmers who were members of the Combined Farmers' Group. Data collection techniques consist of observation, interviews and documentation. While the data analysis technique uses four stages including, data collection, data reduction, data display, and conclusion. \nThe results of this study indicate that the actors involved in the network of organic agriculture development in Batu City consist of government, private, and community actors. These actors carry out interdependent and coordinative relationships. The development of organic agriculture in Batu City also received budget support from the Agriculture and Forestry and Private Service through a capital partnership mechanism. Development of organic agriculture has benefits in terms of social, environmental and economic aspects. While in terms of monitoring and evaluation carried out by the Department of Agriculture to assess the process of organic farming carried out by each farmer group so that Batu City succeeded in producing superior organic commodities. \nBased on these results, it can be concluded that the cooperation between government officials, the private sector and the community based network governance in Batu City has gone quite well. However, it needs to be improved on some obstacles such as the commitment of farmers to implement organic farming; limited capital from farmers; and weak marketing aspects of organic agricultural products. \nkey words : Network Governace, Organic Agricultural
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".