Bibliographic record
Abstract
At the very moment when the United Nations has called for profound shifts in social and economic systems to avert climate catastrophe, state and non-state actors in the United States (U.S.) are using a series of tactics to target and stifle climate protesters. Although the move to stifle climate protesters is often framed as a government effort, this Article argues it is critical to draw out the role of the fossil fuel industry in initiating, amplifying, and supporting such tactics. This Article highlights the role the fossil fuel industry has played in supporting the targeting and restricting of climate protesters in the U.S. The strategies for targeting protesters are grouped into three broad categories, with each category relying on distinctive legal tools. The first category is federal and state legislation that heightens penalties for climate protester in myriad ways. The second is the use of violence and surveillance against climate protesters by both state and non-state actors, which is connected to a rhetorical and legal push to label protesters as extremists and terrorists. The third is retaliatory lawsuits filed against climate protesters and organizations that support climate protests. Although such actions often ostensibly target civil disobedience, by imposing immense criminal and financial consequences, they threaten to unconstitutionally chill lawful, protected protest as well. By examining the tactics in concert, it is much easier to see how both individual protesters and organizations that support protesters might be chilled from participating in lawful climate protest. It is also clear that there are important synergistic effects when these tactics are used together, heightening their respective abilities to undermine and chill climate protest. A third insight is how difficult it is for climate protesters to legally challenge these tactics. Finally, the analysis shows the pivotal role fossil fuel industry trade and lobbying groups play in targeting climate protesters, highlighting the breadth and depth of industry support for such tactics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".