Empirical research on international environmental migration: a systematic review
Bibliographic record
Abstract
This paper presents the findings of a systematic review of scholarly publications that report empirical findings from studies of environmentally-related international migration. There exists a small, but growing accumulation of empirical studies that consider environmentally-linked migration that spans international borders. These studies provide useful evidence for scholars and policymakers in understanding how environmental factors interact with political, economic and social factors to influence migration behavior and outcomes that are specific to international movements of people, in highlighting promising future research directions, and in raising important considerations for international policymaking. Our review identifies countries of migrant origin and destination that have so far been the subject of empirical research, the environmental factors believed to have influenced these migrations, the interactions of environmental and non-environmental factors as well as the role of context in influencing migration behavior, and the types of methods used by researchers. In reporting our findings, we identify the strengths and challenges associated with the main empirical approaches, highlight significant gaps and future opportunities for empirical work, and contribute to advancing understanding of environmental influences on international migration more generally. Specifically, we propose an exploratory framework to take into account the role of context in shaping environmental migration across borders, including the dynamic and complex interactions between environmental and non-environmental factors at a range of scales.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".