What we know and do not know about reciprocal pathways of environmental change and migration: lessons from Ethiopia
Bibliographic record
Abstract
Linkages between environmental change and migration can be reciprocal: declining environmental conditions can trigger people to leave a place, while the movement of people to certain places can have implications for the natural environment and may enhance conflict risks. Although a growing body of research has enriched our knowledge on these two main directions of influence, including the role of conflict, research on dynamic linkages between environmental out-migration and degradation through in-migration is virtually lacking. To fill this gap, we have developed a conceptual framework and have outlined specific pathways of environmental change, migration, immobility, and resource use conflicts. We focus on reciprocal linkages to understand the mechanisms through which environmental change contributes to out-migration and how in-migration, in turn, may contribute to changes in the environment and resource use conflicts. The framework and corresponding pathways are based on our empirical research on resource-dependent rural communities in Ethiopia, which we have embedded in a broader Global South perspective. We identified the following four specific pathways of change: first, environmental change increases migration needs, primarily through declining agricultural production and food insecurity, with financial means and migration experiences being key factors enabling migration. Second, environmental change increases migration needs but hampers migration abilities through care responsibilities and lack of financial resources. This lack inhibits migration and leads to involuntary immobility. Third, migration to rural areas triggers land use change and deforestation through livelihood transitions and adopted land management in receiving areas. Forth, blaming migrants for perceived resource degradation contributes to resource disputes and violence between migrants and the local population. We conclude with future directions for identifying and understanding reciprocal environment-migration linkages and priorities for research.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".