Climate change and migration: A review and new framework for analysis
Bibliographic record
Abstract
Abstract This article presents a new interpretive framework for understanding the implications of climate change for migration, and reviews and reflects on existing evidence and research gaps in light of this framework. Most existing climate‐migration research is heavily environment‐centric, even when acknowledging the importance of contextual or intervening factors. In contrast, the framework proposed here considers five different pathways through which climate change is affecting, or might affect, migration: short‐term shocks, long‐term climatic and related changes, environmental “pull” factors, climate adaptation and mitigation measures, and perceptions and narratives. In reviewing the existing evidence relating to each of these pathways, the paper finds among other things that short‐term shocks may simultaneously increase and reduce migration; that the evidence on long‐term trends provides a weak basis for understanding future dynamics; and that more attention needs to be paid to the other three pathways, by researchers and policymakers alike. Overall, the proposed framework and associated evidence review suggest a different and broader understanding of the migration implications of climate change from that outlined in the IPCC's most recent assessment, or in many existing reviews. This article is categorized under: Climate and Development Knowledge and Action in Development
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".