From “Angry Mobs” to “Citizens in Anguish”: The Malleability of the Protest Paradigm in the International News Coverage of the 2021 US Capitol Attack
Bibliographic record
Abstract
This study tests the robustness of the “protest paradigm”—a routinized, predominantly negative pattern in covering social protest—by examining the news coverage of the 2021 US Capitol attack in eight countries that vary in the nature of their political regime and geopolitical standing, with democratic US allies United Kingdom, Canada, Germany, France, Australia on one side, and authoritarian adversaries Russia, China, and Iran on the other. Based on a computer-assisted analysis of 3,579 news articles, the study shows that rather than operating as a rigid template, the protest paradigm offers national media a malleable set of journalistic devices that can be appropriated to construct the meaning of disruptive global events in a way that reproduces dominant domestic ideologies and advances the ruling elites’ geopolitical interests. In addition to theoretical contribution, the study offers a novel empirical finding to the literature on protest coverage by providing evidence of national media not simply deviating from, but explicitly violating the protest paradigm. As demonstrated by the analysis of the Russian press, rather than delegitimizing the January 6 attackers by making light of their agenda and emphasizing their unruly behavior—which could be expected from coverage consistent with the protest paradigm—the Russian state-owned media trivialized the brutality of the attack by opting for cues with less violent connotations and elevated the legitimacy of the protesters’ actions by framing them as valid demands by politically minded citizens unjustly prosecuted for concerns about the integrity of electoral process.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".