Policing the Climate Crisis: Media Fearmongering and State Repression of Climate Protesters in Australia, Canada, and the United States Within the Post-2016 Conjuncture
Bibliographic record
Abstract
This paper examines the present-day, post-2016 conjuncture of rising authoritarianism and reactionary politics through an analysis of news media and the symbolic criminalization of climate campaigners, Indigenous land and water defenders, and climate justice activists across major publications of record in Australia, Canada, and the United States. Drawing upon Stuart Hall’s influential work on media fearmongering and Othering and his “conjunctural analysis” approach, we examine national news media representations of climate protests with an eye towards wider, political-economic contexts and conditions. This approach allows us to glean insights into a period of profound change while identifying discursive mechanisms of power within and across borders. Through a historically contextualized and cross-national analysis of news reports and opinion commentaries on recent climate protest events, we ultimately reveal how prominent national news outlets in Australia, Canada, and the United States are positioning protesters, and particularly historically marginalized groups who are a part of climate movements, as deviant, criminal, and threatening to the stability and security of the nation-state. We argue that this is significant because these derogatory representations are circulating across borders at the same time as states are specifically targeting and outlawing political, anti-fossil fuel, and infrastructure-oriented forms of climate protest.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".