Income Diversity and Other Socioeconomic Factors That Influence the Household Food Security of Small-Scale Lowland Rice Farmers in Indonesia
Bibliographic record
Abstract
Farmers face many challenges, such as decreased agricultural productivity and decreased household income, which impact farmers' food insecurity.This study aims to analyze the effect of income diversity and other socioeconomic factors on the household food security of smallscale lowland rice farmers.This study uses multivariate logistic regression and input from 264 lowland rice families to explain the relationship between income diversification and other socioeconomic factors on household food insecurity.The results show that household heads who reported higher income diversification tend to be more resistant to food security (OR=11.59;p-value=0.02).Other socioeconomic variables associated with the household food insecurity of lowland rice farmers such as the young age of some farmers, higher education, access to extension services, access to credit, and wider agricultural land lead to a higher chance of reporting high food security (respectively: OR=1.06, p<0.05;OR=2.96, p<0.01;OR=1.69, p<0.01;OR=6.71, p<0.01; and OR=4.08, p<0.01), this happens because these variables affect the productivity of lowland rice.Therefore, increased productivity of lowland rice can have an impact on increasing smallholder household income.Although income diversification is a necessary strategy to improve the food security of lowland rice farmers, it must be accompanied by basic income stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".