Resilience to hazards: rice farmers in the Mahanadi Delta, India
Bibliographic record
Abstract
Developing country deltas are important food producing areas and are home to large numbers of subsistence farmers. In particular, rice farmers dominate the populous deltas of South and Southeast Asia and face frequent climate hazards that have short-and long-term impacts on rice production and livelihoods. The aim of this study is to identify and explain proximal and ultimate factors (land access, cultural practices, and institutional support) that affect rice farmer resilience, that is, to explain why some farmers are more sensitive to climate shocks, why some farmers suffer long-term impacts from climate shocks, and what underlying "ultimate" factors reproduce this vulnerability over time. We undertake this analysis using qualitative interviews and household survey data from two districts in the Mahanadi Delta, Odisha, India. We show that climate hazards cause rice production shocks that are problematic for farmers because rice is predominantly used for household consumption in a context of unreliable off-farm income sources and a lack of insurance and credit. Our research emphasizes that "ultimate" drivers interact with the current mode of rice cultivation to reproduce a low resilience farming state. We argue that agricultural development interventions seeking to make rice farming more resilient to climate hazards should focus on boosting productivity and shock-resistance, but also be cognizant of the system within which rice farming is practiced and the contextual "ultimate" factors that reproduce vulnerability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".