‘Power-sensitive design principles’ for climate change adaptation policy-making in South Asia
Bibliographic record
Abstract
Despite the proliferation of power approaches to study climate change, there is little focus on how to deal with the negative effects of power in climate change adaptation (CCA) policy-making. CCA literature provides little insight into understandings of manifestations of power that can create negative effects, especially in the context of South Asia. This review answers the question: How can CCA policy actors deal with the negative effects of power during the policy-making process? We used a two-layered systematic literature review to identify various manifestations of power that are responsible for negative effects in CCA policy-making in South Asia and to determine power-sensitive design principles (PDPs) to address these manifestations of power. We conclude that although the four PDPs are no panacea for dealing with the negative manifestations of power, they are useful considerations when engaged in long-term CCA policy processes.
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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 | 0.085 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".