KESESUAIAN SOSIAL EKONOMIPERLINDUNGAN LAHAN PERTANIAN PANGAN BERKELANJUTAN DI KABUPATEN KUNINGAN
Bibliographic record
Abstract
Economic development in Indonesia since 1980s is dealing with conversion of agricultural land to industry, housing, and other sector in city and its periphery. Land conversion have a great impact to food production rather than the impact from technical problem (drought and pest problem). Government need to preserve agricultural land in order to maintain food production. Thus government made a mandatory approach byissued Law No. 41 year 2009. The aim of this research are to identify an actual socioeconomic characteristics in the area of land preservation program (LP2B) in Kuningan Regency, to identify farmers perception on LP2B and to analyze socioeconomic suitability in the areaof LP2B program. Data were analyzed by descriptive statistics and likert scale. Based on the result, there are nine socioeconomic indicator on land preservation program (LP2B) in Kuningan Regency, namely; land conversion rate, food balance, disparity between farm and non-farm income, agriculture households, agriculture labor, farmers’ groups, spatial planning policies and farmers perceptions. Farmers have a positive perception on LP2B program. Land preservation program (LP2B) priority should be donein Cilimus sub district due to low support of socio economic characteristic. Meanwhile Ciawigebang and Cibingbin sub district become a next priority of preservation. Keyword : farmer’s perception, food security, land conversion, socioeconomic of LP2B
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".