Bibliographic record
Abstract
The first part of this article examines remedies granted in climate change litigation against governments in domestic and supranational courts.It concludes that courts have tended to grant focused and modest remedies.Requests for overly ambitious remedies have not been successful and may have caused North American courts to hold human rights claims based on climate change to be non-justiciable.The second part of this article examines the range of available judicial remedies and their strengths and weaknesses.It identifies interim relief, the "declaration plus," and remedies directed towards laws that violate human rights as promising remedial strategies.The third part proposes a number of remedial principles.It argues for a two-track remedial approach that combines immediate remedies directed at particular harms with dialogic and interactional remedies in which courts engage with other institutions and parties to produce longer-term systemic remedies that will curb emissions in the future.Courts should explicitly use proportionality reasoning when factoring in competing social interests and confronting polycentric problems.Bi-jural remedies that combine human rights and Indigenous law are also promising.Litigants should expect that no one case will remedy the threatening tides of climate change.They should pursue cycles of remedies where new and more intense remedies are used to respond to remedial failures and continued violations of human rights related to global warming.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".