Sustainability Transitions: Exploring Risk Management and the Future of Adaptation in the Megacity of Lagos
Bibliographic record
Abstract
Lagos, a coastal megacity with more than 11 million inhabitants faces serious development challenges in addition to climatic risks and extreme weather events. There are uncertainties about future disaster risk trends and about how to manage and adapt to existing threats in ways that ensure a just and sustainable development trajectory. In this paper, we explore the changes that have occurred in risk management in Lagos over the last 20 years, as part of a broader endeavor towards sustainability. We draw on transition theory to analyze data collected from a scenario workshop and expert interviews conducted over a period of two years, to understand the influences, processes and actors that shape the adaptation-development nexus in Lagos. Findings based on stakeholders voices present a risk management regime firmly oriented towards protecting contemporary development gains and policies, despite Nigeria’s contested development strategy. Future positioning of risk management is described as either maintaining its current goals or shifting towards a position where development is seen as a root cause of risk and a focus for change. Resilience (marginal changes in development to maintain stability) is not foreseen as a likely future choice for Lagos. This is in contrast to many global agendas that promote resilience and reflects the realities of managing risks in the context of contested 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".