Powering and puzzling: climate change adaptation policies in Bangladesh and India
Bibliographic record
Abstract
Abstract South Asia is a region uniquely vulnerable to climate-related impacts. Climate change adaptation in India and Bangladesh evolves using powering and puzzling approaches by policy actors. We seek to answer the question:how do powering and puzzling approaches influence the climate change adaptation policy design and implementation processes in Bangladesh and India?We adopted two strategies to collect and analyze data: semi-structured interviews and discourse analysis. We found that adaptation policymaking is largely top-down, amenable to techno-managerial solutions, and not inclusive of marginalized actors. In Bangladesh, power interplays among ministerial agencies impair the policy implementation process and undermine the success of puzzling. Local-scale agencies do not have enough authority or power to influence the overall implementation processes occurring at higher scales of governance. The powering of different actors in Bangladesh is visible through a duality of mandates and a lack of integration of climate adaptation strategies in different government ministries. The powering aspect of India’s various adaptation policies is the lack of collective puzzling around the question of differentiated vulnerability by axes of social difference. Paradoxically, India has a puzzling approach ofhiding behind the poorin international negotiations. Moving forward, both countries should strive to have more inclusive and equitable adaptation policymaking processes that enable the participation of marginalized populations and represent their anxieties and aspirations. Identifying policy-relevant insights from South Asia using the powering and puzzling approaches can foster adaptation policy processes that facilitate empowerment, the missing piece of the adaptation policymaking puzzle.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".