Addressing the risk of maladaptation to climate change
Bibliographic record
Abstract
This paper reviews the current theoretical scholarship on maladaptation and provides some specific case studies—in the Maldives, Ethiopia, South Africa, and Bangladesh—to advance the field by offering an improved conceptual understanding and more practice‐oriented insights. It notably highlights four main dimensions to assess the risk of maladaptation, that is, process, multiple drivers, temporal scales, and spatial scales. It also describes three examples of frameworks—the Pathways , the Precautionary , and the Assessment frameworks—that can help capture the risk of maladaptation on the ground. Both these conceptual and practical developments support the need for putting the risk of maladaptation at the top of the planning agenda. The paper argues that starting with the intention to avoid mistakes and not lock‐in detrimental effects of adaptation‐labeled initiatives is a first, key step to the wider process of adapting to climate variability and change. It thus advocates for the anticipation of the risk of maladaptation to become a priority for decision makers and stakeholders at large, from the international to the local levels. Such an ex ante approach, however, supposes to get a clearer understanding of what maladaptation is. Ultimately, the paper affirms that a challenge for future research consists in developing context‐specific guidelines that will allow funding bodies to make the best decisions to support adaptation (i.e., by better capturing the risk of maladaptation) and practitioners to design adaptation initiatives with a low risk of maladaptation. WIREs Clim Change 2016, 7:646–665. doi: 10.1002/wcc.409 This article is categorized under: Vulnerability and Adaptation to Climate Change > Learning from Cases and Analogies
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".