Climate-induced Migration in South Asia: Migration Decisions and the Gender Dimensions of Adverse Climatic Events
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
There is significant interest in determining the role of climate-induced shocks as a \nprominent driver on migration decisions of different groups of farmers in South \nAsia. Using data from a survey of 2,660 farm-families and focused group \ndiscussions in Bihar (India), Terai (plains) (Nepal) and coastal Bangladesh, we \nemployed logistic regression to investigate household response towards migration \nand gender dimensions of adverse climatic events. The results suggest that migration \ndecisions depend on farmers’ unique resource profiles: (a) households that use \nmigration to improve their resilience, mostly resource rich households; (b) \nhouseholds that have no alternative but to migrate, mostly poor farmers; and (c) \nhouseholds who cannot migrate due to different socio-economic obligations, mostly \nfarmers with intermediate level of income that also includes women, children and \nelderly of different income profiles. These profiles represent a spectrum with \nhouseholds within a profile being closer to one or the other of the profiles on either \nside. They are not mutually exclusive and serve as a point of departure for further \nresearch to refine key explanatory variables. Given that some members of the \nhousehold pursue migration as a result of adverse climatic events, government \nstrategies are required to mitigate risks at destinations and create opportunities for \nthe trapped populations.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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