Living on the Edge: How Nigeria’s Slum Dwellers are Both Victims and Drivers of Climate Change?
Bibliographic record
Abstract
Nigeria’s slum dwellers, particularly in settlements like Makoko and Port Harcourt, face escalating threats from climate-amplified flooding, air pollution, and extreme heat, hazards disproportionately borne by the urban poor. Drawing on multiple cases and existing evidence, this article explores how systemic neglect and exclusionary urban policies compel residents to adopt survival strategies such as waste burning and charcoal use, which unintentionally exacerbate environmental degradation through emissions and deforestation. These challenges are compounded by limited access to clean energy, waste infrastructure, and healthcare, creating a cycle of vulnerability. Yet, across these communities, grassroots innovations from Makoko’s floating school to informal waste-to-wealth models reveal localized capacities for climate adaptation. Still, structural barriers, including forced evictions, mismanaged funds, and elite-driven urban planning, obstruct the institutional support these initiatives require. By connecting community responses with broader governance failures, this study exposes the need for inclusive development approaches that position slum residents not as passive recipients of aid but as essential actors in climate resilience. Institutionalizing community-led solutions and scaling their impact will be critical to reducing urban climate vulnerability in Nigeria and similar contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".