Moving in and out of vulnerability: Interrogating migration as an adaptation strategy along a rural–urban continuum in India
Bibliographic record
Abstract
Abstract Migration is a key livelihood strategy to diversify incomes, reduce risks associated with rainfed agriculture and the effects of climate change, and meet personal aspirations. Drawing on life history interviews with migrant and non‐migrant families, we explore the role of migration and commuting in addressing livelihood vulnerability along a rural–urban continuum in Karnataka, India. We find that labelling migration as an adaptation strategy or not does not necessarily capture the breadth of experiences and implications for livelihoods that migrants and their families face. At an intra‐household level, migration and commuting can alleviate vulnerability for some family members while exacerbating vulnerability of others. At a larger scale, migration that is adaptive at a household scale can be maladaptive at a system scale, where cities are unable to provide for or absorb migrants who often live in highly vulnerable conditions. Finally, on a temporal scale, migration and commuting affect livelihood trajectories and choices beyond the migrants alone, and understanding how these strategies affect household vulnerability over time is crucial for adaptation research. We also highlight the use of life histories as a methodological tool that complements current econometric approaches exploring migration and allows for in‐depth and temporally sensitive inquiry into the drivers and consequences of migration.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".