Intertwined people–nature relations are central to nature-based adaptation to climate change
Bibliographic record
Abstract
Adaptation to climate change is a social-ecological process: it is not solely a result of natural processes or human decisions but emerges from multiple relations within social systems, within ecological systems and between them. We propose a novel analytical framework to evaluate social-ecological relations in nature-based adaptation, encompassing social (people-people), ecological (nature-nature) and social-ecological (people-nature) relations. Applying this framework to 25 case studies, we analyse the associations among these relations and identify archetypes of social-ecological adaptation. Our findings revealed that adaptation actions with more people-nature relations mobilize more social and ecological relations. We identified four archetypes, with distinct modes of adaptation along a gradient of people-nature interaction scores, summarized as: (i) nature control; (ii) biodiversity-based; (iii) ecosystem services-based; and (iv) integrated approaches. This study contributes to a nuanced understanding of nature-based adaptation, highlighting the importance of integrating diverse relations across social and ecological systems. Our findings offer valuable insights for informing the design and implementation of adaptation strategies and policies.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".