A Systematic Review of International and Internal Climate-Induced Migration in Africa
Bibliographic record
Abstract
Academics and policymakers have been paying close attention to the impact of climate change on migration in recent years. This phenomenon piqued interest because the factors driving environmentally induced migration are complex and manifold. Noticeably, there has been considerable scholarship on climate change and migration in Africa. However, there has not been a concerted effort to periodically review the existing literature to systematically document the state of scholarship. Using a standardized systematic review procedures to analyze 22 peer-reviewed studies published between 2000 and 2022, we found that climate change impacts migration in many complex and multilayered forms. Beyond what has already been established in the literature on climate-related migration such as environmental effects on migration; migration as an adaptation strategy; and the influence of environmental and non-environmental factors on migration; we also found that (1) studies on climate-induced migration in Africa intensely focused on SSA, suggesting an uneven study of the region, (2) heavily affected people tend to be immobile, and (3) young people have high migration intentions due to harsh climate insecurities. These findings require urgent government and stakeholder attention. Specifically, there is a need for scholarship to interrogate the climate change–immobility nexus in order to design appropriate in situ or ex situ adaptation strategies to support lives and livelihoods.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".