Exploring the nexus between natural disasters and food (in)security: Evidence from rural Bangladesh
Bibliographic record
Abstract
Abstract The Sustainable Development Goals (SDG) emphasise the reduction of poverty, hunger, and food insecurity as prerequisites for the economic development of a country. This paper examines how natural disaster shocks affect the food security of rural households in Bangladesh. We utilise the latest edition of the Bangladesh Integrated Household Survey (BIHS) produced by the International Food Policy Research Institute to understand the determinants of food security. In contrast to the existing literature, we use the Food Insecurity Experience Scale (FIES) to measure household food security. The empirical result from an ordered logit regression suggests that households that are exposed to natural disaster shocks are more likely to be food insecure compared with households that have not been exposed to such shocks. Furthermore, international remittances increase food security, while domestic remittances do not significantly affect household food security. The study also found that the marital status and education of the household head, household indebtedness, household size, education, expenditures and landownership significantly affect food security. Our findings underscore the importance of investing in the development of infrastructure and food storage facilities in rural communities to tackle food insecurity. Moreover, increasing technical knowledge and improving the quality of education are vital to strengthen food security in Bangladesh.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".