Can labour migration help households adapt to climate change? Evidence from four river basins in South Asia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it