Climate Resilience in African Coastal Areas: Scaling Up Institutional Capabilities in the Niger Delta Region
Bibliographic record
Abstract
African coastal areas are increasingly prone to coastal challenges. The Niger Delta coastal areas are exposed to physical alterations due to natural and anthropogenic influences. In addition to current and projected extreme events such as flooding, erosion, sea-level rise, and heat waves, other conflicting factors increasing the vulnerability of the coastal Niger Delta range from the rapid shift in demography, urbanization, unsustainable land use, and inadequate implementation of relevant policies to oil spillage and gas flaring. All these issues, in addition to climate variability, increase the vulnerability and threaten the resilience of the human and natural environment. This chapter highlights the effects of climate- and weather-related extremes in the vulnerable riparian Niger Delta, based on existing facts and an empirical study, which gives insight on institutional challenges derived from the views of relevant technocrats, nongovernmental organizations, and stakeholders. Analysis of stakeholder views indicates some weaknesses and potential strengths of relevant institutions in addressing climate change issues through effective governance. Hence, scaling up institutional capabilities would enhance the resilience of communities and improve adaptive capacities. Key strengths involve employing existing institutional frameworks under relevant MDAs to climate-proof future coastal, riverbank, or lakeshores 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".