The “Adaptation Paradox” and Citizen Ambiguity over Government Climate Policies: Survey Findings from Bangladesh
Bibliographic record
Abstract
National governments in the world’s most climate-vulnerable nations are using domestically sourced and international funding and expertise. However, local governments are where citizens in many developing countries turn to solve problems. Using results from a nationally representative sample in Bangladesh, one of the most climate-vulnerable nations in the world, we examine citizens’ perspectives about the responsibility of different levels of government to address climate change problems. Inasmuch as Bangladeshi survey respondents do draw distinctions, they trust local governments more than the national one. However, local governments tend to be relatively weak vis-à-vis the national government: political and financial resources are concentrated there, and the national government has access to the resources of international financial institutions. Furthermore, respondents tend to view local officials as embedded community networks more than as formal government agents. We conclude that better public communications across levels of government with vulnerable communities are needed if these communities are to protect themselves from extreme weather events, access services, and reap the benefits of “polycentric” climate adaptation governance across a full range of levels.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".