Four Principles of Transformative Adaptation to Climate Change‐Exacerbated Hazards in Informal Settlements
Bibliographic record
Abstract
Residents of urban informal settlements are among the most at-risk of climate change-exacerbated hazards. Yet, traditional approaches to adaptation have failed to reduce risk sustainably and equitably. In contrast, transformative adaptation recognizes the inextricable nature of complex climate risk and social inequality, embedding principles of social justice in pathways to societal resilience. Its potential for impact may be greatest in informal settlements, but its application in this context introduces a new set of challenges and remains largely aspirational. To address this missed opportunity, in this focus article we provide clarity on how transformative adaptation can manifest in informal settlements. Although context-dependency precludes the formulation of specific guidelines, we identify four principles which are foundational to its deployment in these settings. Acknowledging constraints, we define levels of achievement of the principles and suggest how they might be reached in practice. Achieving transformative adaptation in informal settlements is complex, but we argue that it is already achievable and could represent a prime opportunity to accelerate the rate of adaptation to build a climate resilient society.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.068 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".