Risks and responses in rural India: Implications for local climate change adaptation action
Bibliographic record
Abstract
People in drylands face multiple climatic and non-climatic risks and subsequently engage in various response strategies to manage these risks. Research on risk management has typically focussed on a static, location-specific understanding of risk and response. However, empirical evidence suggests that risks and vulnerability vary across space and time. Increasingly, responses traverse multiple locations e.g. people move across rural and urban areas, women move beyond the household/community to earn additional incomes. To highlight this dynamic reality of risks and responses, we study livelihood transitions in South India. We unpack risk and response portfolios across scales – household, community, and sub-national (district) levels – and classify them as coping, adaptive and maladaptive. Our findings emphasise that present responses do not necessarily qualify as climate change adaptation strategies. While certain strategies do improve household wellbeing in the short run, there is relatively lower evidence to suggest an increase in adaptive capacity to deal with climatic risks in the future. These findings point to critical gaps in understanding current risk management and how it can contribute to local adaptation policymaking and implementation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".