Drivers of migration intentions in the Volta Delta: Investigating the effect of climate-related hazards and adaptation strategies
Bibliographic record
Abstract
The decision to migrate involves multiple causes and motivations with environmental factors subsumed by economic and other dimensions. Deltas are rich in natural resources but are also vulnerable to environmental hazards making them a hotspot for out-migration. In spite of some infrastructural interventions, specifically in the Volta Delta, to minimize the environmental effects, there is scant evidence of their impacts on livelihoods and the potential to reverse out-migration trends and aspirations. Additionally, there is little knowledge on the key drivers of migration in the area. Using data from the 2016 DECCMA household survey in Ghana, we found that exposure to drought does not trigger migration intentions, however, exposure to erosion and salinity do. Households capable of diverse adaptation options have a higher likelihood of migration intention. Households whose main livelihood is ecosystem-based were less likely to have the intention to migrate compared with those whose livelihoods were non-ecosystem based. The study provides insights into future migration intentions and drivers of migration in the Volta Delta.
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How this classification was reachedexpand
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 | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".