Uncertainty and the epistemic dimension of democratic deliberation in climate change adaptation
Bibliographic record
Abstract
Climate change adaptation is inherently local, contextual, and political, a problem distinct from the global collective action problem that is climate stabilization. Climate change vulnerability is a function not only of hydro-meteorological changes, specific geographic contexts, ecosystem integrity, and economic poverty, but also of the social and political institutions of a given local or regional context. The uncertainty, complexity, and inherent politics of climate change adaptation in particular places mean that adaptive institutions, if they are to be more than just disaster prediction and response mechanisms, must be flexible, dynamic and capable themselves of adapting quickly to changing environmental, economic, and social conditions. Certain approaches to adaptation have moved away from rigid orthodox development models. Nonetheless, they are often so general as to be impractical, or in effect comprise repackaged concepts and methods borrowed from climate change mitigation efforts and international development institutions. This study discusses the epistemic dimension of democracy at the level of international environmental institutions and at the level of local, contextually unique adaptation projects. Development and adaptation practices that involve democratic participation do so largely in accordance with norms of fairness and justice, which are unquestionably important. Attaching democracy exclusively to transcendent norms of justice, however, belies concrete possibilities for democratic approaches to climate adaptation. These possibilities reside in the epistemic dimension of democracy, a pragmatic notion developed. The account suggests that an epistemic democratic conceptual framework can inform adaptation institutions that are better able to cope with complexity and uncertainty, even ultimately directing these lessons towards the international sphere's institutional focus on mitigation.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".