Beyond the risks to food availability – linking climatic hazard vulnerability with the food access of delta-dwelling households
Bibliographic record
Abstract
Abstract Although climate-driven hazards have been widely implicated as a key threat to food security in the delta regions of the developing world, the empirical basis of this assertion has centred predominantly on the food availability dimension of food security. Little is known if climatic hazards could affect the food access of delta-resident households and who is likely to be at risk and why. We explored these questions by using the data from a sample of households resident within the Ganges-Brahmaputra-Meghna (GBM) delta in Bangladesh. We used an index-based analytical approach by drawing on the vulnerability and food security literature. We computed separate vulnerability indices for flood, cyclone, and riverbank erosion and assessed their effects on household food access through regression modelling. All three vulnerability types demonstrated significant negative effects on food access; however, only flood vulnerability could significantly reduce a household’s food access below an acceptable threshold. Households that were less dependent on natural resources for their livelihoods – including unskilled day labourers and grocery shop owners – were significantly more likely to have unacceptable level of food access due to floods. Adaptive capacity, measured as a function of household asset endowments, proved more important in explaining food access than the exposure-sensitivity to flood itself. Accordingly, we argue that improving food security in climatic hazard-prone areas of developing country deltas would require moving beyond agriculture or natural resources focus and promoting hazard-specific, all-inclusive and livelihood-focused asset-building interventions. We provide an example of a framework for such interventions and reflect on our analytical approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".