Everyday adaptation, interrupted agency and beyond: examining the interplay between formal and everyday climate change adaptations
Bibliographic record
Abstract
Climate change is increasingly widespread and intense. In response, formal adaptation efforts are gaining momentum and financing globally, while those affected address felt changes through a variety of everyday adaptations, the aggregate daily practices articulated in response to ongoing social-ecological change. Our research examined the interplay between formal and everyday adaptations in practice. Specifically, we sought to shed light on the tendency emerging in adaptation literature of what we term interrupted agency, where formal adaptation interventions interrupt everyday adaptation strategies—and agency—of local actors, potentially leading to maladaptation. We did so in North Central Vietnam, where climate change is disrupting lives and livelihoods, and numerous formal and everyday adaptation measures are being implemented in response. We examined three key climate-affected sectors, agriculture, water management, and coastal management, drawing on existing literature as well as interviews and document and policy review. We found that differences in formal and everyday adaptations can indeed lead to interrupted agency yet, in some instances, also support complementarities and even transformative change. Such outcomes required dialogue and pluralistic input to adaptation-related policy, practice, and decision-making, underlining the importance of attention to participation, representation, and influence in decision-making in adaptation efforts. Our exploration of the concepts of everyday adaptation and interrupted agency illustrates that these can valuably contribute to adaptation literature, particularly on the politics of adaptation.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| 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".