Uncertainty and Tension in Constitutional Adjudication of Climate Mitigation
Bibliographic record
Abstract
Abstract In recent years, courts across the world have increasingly held governments accountable for addressing climate change. While such rulings have fueled optimism about constitutional law as a vehicle for climate ambition, this Article argues that the role of constitutional law in advancing climate goals is far more complex and contested. Constitutions encapsulate diverse and sometimes conflicting values, which can create tensions when courts adjudicate climate policies. As government climate measures become more concrete, conflicts arise between rights, institutional structures, and political realities. Drawing on examples from Germany, Canada, and Mexico, this Article highlights the challenges of adjudicative uncertainty, the underspecificity of constitutional norms, and the polyvocality of constitutional values in the context of climate change. This Article concludes with recommendations for judges to adopt a principled, context-sensitive approach to constitutional climate adjudication, balancing the urgency of climate action with the complexities of state capacity and constitutional structures.
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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 | 0.152 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.016 | 0.048 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.016 |
| 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".