Migration and Household Adaptation in Climate-Sensitive Hotspots in South Asia
Bibliographic record
Abstract
Abstract Purpose of Review South Asia is highly vulnerable to the impacts of climate change, owing to the high dependency on climate-sensitive livelihoods and recurrent extreme events. Consequently, an increasing number of households are adopting labour migration as a livelihood strategy to diversify incomes, spread risks, and meet aspirations. Under the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) initiative, four research consortia have investigated migration patterns and their inherent linkages to adaptation to climate change in climate hotspots. This article synthesizes key findings in regional context of South Asia. Recent Findings The synthesis suggests that in climate-sensitive hotspots, migration is an important livelihood diversification strategy and a response to various risks, including climate change. Typically, one or more household members, often young men, migrated internally or internationally to work in predominantly informal sectors. Remittances helped spatially diversify household income, spread risks, and insure against external stressors. The outcomes of migration are often influenced by who moves, where to, and what capacities they possess. Summary Migration was found to help improve household adaptive capacity, albeit in a limited capacity. Migration was mainly used as a response to risk and uncertainty, but with potential to have positive adaptation co-benefits.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".