Powering and puzzling: climate change adaptation policies in Bangladesh and India
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 of hiding behind the poor in 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it