Climate Change Litigation and Narrative: How to Use Litigation to Tell Compelling Climate Stories
Bibliographic record
Abstract
The U.S. government has not taken sufficient action to mitigate the threat of dangerous climate change.Frustrated by the lack of action in the legislative and executive branches, climate advocates turn to the judicial branch and litigation to advance their cause.Litigation is important not only for its ability to create substantive legal change, but also for its power to generate media coverage and shape public and political discourse.Research from across the social sciences highlights key psychological challenges that can prevent the U.S. public from engaging with the science of climate change, understanding the risks posed by climate change, and feeling motivated to take corrective action.Research also shows that the way in which a public health issue is framed powerfully shapes the public debate and policy prescriptions for that issue.This Article examines how climate advocates can construct their litigation messaging in light of this research to most effectively advance the climate movement in the United States.If used effectively, the medium of litigation offers a unique opportunity to reframe climate change and overcome some of the public's cognitive hurdles to perceiving the true dangers of climate change.The structure of litigation, which requires plaintiffs to trace their injuries-including economic, social, and health-related injuries-to the actions of defendants, allows climate advocates to leverage insights from the social sciences to make their climate change narratives as salient as possible.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.029 | 0.046 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".