Culture as a mediator of climate change adaptation: Neither static nor unidirectional
Bibliographic record
Abstract
Abstract Though there is increasing recognition of the cultural dimensions that shape climate change adaptation, our experience from working with actors engaged in adaptation policy and practice suggests that the role of culture still tends to be conceived in overly narrow and fixed terms. This is exemplified in portrayals of conservative cultural norms as stifling positive change. A growing body of research across the world indicates that the reality is seldom as simple as this—culture works in complex and variable ways, and, most importantly, is inherently dynamic. Drawing especially from research work on vulnerability and adaptation conducted in semi‐arid regions, we illustrate this argument by briefly exploring three themes—multiple knowledge systems for farming in Botswana, the dynamics of pastoralist values and livelihoods in Kenya, and the interplay of caste and livelihood choices in India. Understanding how different facets of culture such as these operate in context helps move away from viewing culture statically as a barrier or enabler, and toward a more plural and dynamic appreciation of the role of culture in adaptation. This includes recognizing the potential for factors that may be construed as barriers to become enablers. Critical, balanced engagement with cultural dimensions in both research and practice, understanding and working with these dynamic social structures, is essential if adaptation is to create meaningful and lasting change for those who need it most. This article is categorized under: Vulnerability and Adaptation to Climate Change > Values‐Based Approach to Vulnerability and 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".