Navigating Climate-related Displacements: Insights from Cyclone Idai in Mozambique
Bibliographic record
Abstract
Tropical Cyclone Idai, which struck in March 2019, was among the most catastrophic climate-related disasters in Southern Africa, causing widespread destruction, loss of life, economic loss, and rapid-onset displacement in Mozambique, Malawi, and Zimbabwe. Displaced populations experienced disproportionate impacts after the cyclone, yet few studies have examined post-disaster displacement management dynamics. As climate change intensifies cyclones like Idai and their associated displacement impacts, addressing this research gap is crucial for mitigating future climate-related displacement risks. Drawing on peer-reviewed literature, reports from humanitarian agencies, and policy documents, this paper examines Cyclone Idai-related displacement in Mozambique, with a focus on the short-term, long-term, and cross-border dimensions shaped by displacement management. Findings indicate that displacements following Cyclone Idai were largely internal and marked by weak disaster preparedness, reactive disaster response, and resettlement, which are entrenched in structural and non-structural vulnerabilities. There was also limited coordination on cross-border displacements. Altogether, these challenges undermined disaster recovery, heightening future displacement risks. Based on these findings, the paper recommends a shift towards improved early warning systems, disaster preparedness, effective coordination, and mobilisation of local resources at national and regional levels for disasters. Furthermore, durable, displacement-proof solutions must be underpinned by coherent national and regional frameworks for displacement management that integrate disaster risk reduction into land-use planning, urban planning, and climate-resilient 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".