The Climate Necessity Defense: Protecting Public Participation in the US Climate Policy Debate in a World of Shrinking Options
Bibliographic record
Abstract
Scholars have documented how since 1989 the climate change counter-movement, a densely connected and well-funded network of fossil fuel industry members and their allies, has worked to stymie government action on climate change. Recent allegations that key actors in the climate change counter-movement, including Exxon Mobil, actively misled the public on the science of climate change have given rise to litigation and investigations by states attorney general. At the same time, climate protesters have been facing violence, harassment, and legislative crackdowns. Some climate protesters facing criminal charges for civil disobedience are attempting to use the climate necessity defense in court. The essential thrust of the climate necessity defense, an affirmative defense to criminal charges arising from civil disobedience, is that the harm of the defendants’ disobedience is far outweighed by the harms being protested. This article sketches some initial connections between the influence of the climate change counter-movement, the crackdown on climate protesters, and the importance of the climate necessity defense. In doing so, it highlights the shrinking options available to members of the public to participate in the debate over climate policy, underscoring why some might feel compelled to engage in civil disobedience. Finally, it briefly discusses the climate necessity defense and argues that it is an important tool to help ensure the US public has an effective voice in climate policy.
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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 | 0.037 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.044 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.003 | 0.027 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".