Can labour migration help households adapt to climate change? Evidence from four river basins in South Asia
Bibliographic record
Abstract
The study focuses on four river basins, Gandaki, Indus, Upper Ganga and Teesta, in the Hindu Kush Himalayan (HKH) region in South Asia. The region is considered one of the more environmentally vulnerable areas in the world due to recurrent natural hazards that can be exacerbated by future climate change. The dependence of the population on natural resources based livelihoods makes the region particularly vulnerable to adverse climate change impacts. Labour migration can help household adaptation, particularly when it incurs significant cash investment. The paper analyses the determinants of household adaptation, including migration, in three sectors, namely, agriculture, livestock, and water. It shows that household adaptation to the negative effects of climate change was very poor in the region, with less than a third of the households undertaking adaptation measures. While labour migration showed a positive influence on household adaptation, it was statistically significant only in agriculture. Nevertheless, migration influenced household adaptation indirectly through livelihood diversification, access to services provide of external stakeholders, and changes in household composition. The study identified location, access to climate information, and services provided by external stakeholders as important factors in household adaptation to climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".