Climate-induced Migration in South Asia: Migration Decisions and the Gender Dimensions of Adverse Climatic Events
Bibliographic record
Abstract
There is significant interest in determining the role of climate-induced shocks as a prominent driver on migration decisions of different groups of farmers in South Asia. Using data from a survey of 2,660 farm-families and focused group discussions in Bihar (India), Terai (plains) (Nepal) and coastal Bangladesh, we employed logistic regression to investigate household response towards migration and gender dimensions of adverse climatic events. The results suggest that migration decisions depend on farmers' unique resource profiles: (a) households that use migration to improve their resilience, mostly resource rich households; (b) households that have no alternative but to migrate, mostly poor farmers; and (c) households who cannot migrate due to different socio-economic obligations, mostly farmers with intermediate level of income that also includes women, children and elderly of different income profiles. These profiles represent a spectrum with households within a profile being closer to one or the other of the profiles on either side. They are not mutually exclusive and serve as a point of departure for further research to refine key explanatory variables. Given that some members of the household pursue migration as a result of adverse climatic events, government strategies are required to mitigate risks at destinations and create opportunities for the trapped populations.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".