Power interplay between actors in climate change adaptation policy-making in South Asia
Bibliographic record
Abstract
To address the impacts of climate change, countries in South Asia are increasingly making efforts to design climate change adaptation (CCA) policies. Such policies are prepared in a complex power-loaded environment, where different policy actors struggle with one another to meet their personal or collective interests. Current CCA policy research highlights the importance of power in policy-making, but few studies have looked into this systematically. This dissertation therefore aims to study the role of power in CCA policy-making in South Asia and to recommend ways to deal with the negative effects of power. The research adopts an abductive research design, using qualitative case studies in Nepal, India and Bangladesh. Case studies at local, national and transboundary level are conducted to study how actors' power interplay influences CCA policy-making processes and policies. This dissertation proposes a power interplay framework that describes interactions between policy actors and how actors deploy material and ideational resources to influence one another. It presents evidence of how policy actors use these resources at local and regional level to exclude local policy actors and push for short-termism in local adaptation plans and in the planning process for the transboundary Brahmaputra River basin. It shows that multiple climate policy paradigms shape the way in which adaptation is framed and approached by policy actors and that these paradigms have an important influence on how power interplay evolves. To reduce the negative effects of power – for example, the exclusion of actors – I distilled four power-sensitive design principles (PDPs) from other social science disciplines for application in the CCA context. The results of this dissertation offer theoretical contributions to the study of CCA policy-making in South Asia, and concrete and practically relevant PDPs are proposed to improve long-term CCA planning.
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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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".