Climate Change Impacts and Internal Migration in Africa:A Scoping Review of Emerging Evidence
Bibliographic record
Abstract
Climate change has emerged as a significant driver of internal migration in Africa, transforming longstanding patterns across rural and urban areas. This scoping review assesses the nexus between climate change and internal migration across this broad region by synthesising existing literature. It examines selected studies to illustrate how extreme weather events and slow-onset processes are driving population mobility within national borders. The review differentiates between voluntary and involuntary forms of migration, their duration, geographical trajectories, and socioeconomic implications for sending and receiving areas. Gender, ethnicity, and socioeconomic status are treated as key mediating variables of vulnerability, exposure to adverse impacts, decision-making, and (individual and household) adaptive capacity in the context of environmental stressors. The vulnerabilities of populations in marginal environments, such as arid zones, low-lying coastal areas, and conflict-prone regions, are also taken into account when assessing migration propensities. Finally, the review identifies gaps in existing literature, including underexplored themes and neglected geographical areas, and reflects on the policy implications of the analysis
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".