Determinants of Food Expenditure and Household Income in Gunungkidul’s Karst Region
Bibliographic record
Abstract
Poverty is closely linked to both household income and food security. This condition is related to the insufficient purchasing power that people have to have access to food. The purpose of this study is to determine the determining factors of food expenditure and incomes of agricultural households in the karst region of Gunungkidul, Indonesia. The broader implications occur when the low welfare of farmers (with indicators of household income and food security) results in a declining human development index that hampers regional development. Due to the high level of poverty, the study is located in the karst mountainous region of Gunungkidul Regency. Soil infertility affects agricultural production, making it sub-optimal. Multiple linear regressions are used to estimate this study's findings by employing ordinary least squares (OLS). The research data uses primary data with questionnaires to respondents. The study concludes that the estimated parameters of farm household income, off-farm income, remittances, total household income and non-food expenditure are significantly correlated with total food expenditure. The estimated parameters of education, age, assets, remittances and off-farm employment of the head of the household are significantly correlated with total household income. Therefore, off-farm income and remittances contribute to the increase in total household income, alleviation of food insecurity and reduction of poverty in Karst Gunungkidul. The contribution of this research is that the results obtained can be taken into consideration for policy makers or local stakeholders to pay attention to significant determinants of total food expenditure and total household income.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 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".