A global assessment of actors and their roles in climate change adaptation
Bibliographic record
Abstract
Abstract An assessment of the global progress in climate change adaptation is urgently needed. Despite a rising awareness that adaptation should involve diverse societal actors and a shared sense of responsibility, little is known about the types of actors, such as state and non-state, and their roles in different types of adaptation responses as well as in different regions. Based on a large n -structured analysis of case studies, we show that, although individuals or households are the most prominent actors implementing adaptation, they are the least involved in institutional responses, particularly in the global south. Governments are most often involved in planning and civil society in coordinating responses. Adaptation of individuals or households is documented especially in rural areas, and governments in urban areas. Overall, understanding of institutional, multi-actor and transformational adaptation is still limited. These findings contribute to debates around ‘social contracts’ for adaptation, that is, an agreement on the distribution of roles and responsibilities, and inform future adaptation governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".