The Spillover of the US Capitol Insurrection: Reducing Expressed Support for Domestic Far-Right Parties
Bibliographic record
Abstract
Abstract Although far-right insurrections often catch worldwide attention, little is known about whether and how these autocratization events affect other countries. This article studies the spillover of a prototype of such coup attempts – the January 6th Capitol insurrection. I argue that a far-right insurrection abroad can trigger a transnational learning process, which increases the salience of the far-right’s anti-democratic potential. Consequently, due to shaming and changes in voting calculus, citizens are less likely to support a domestic far-right party. To test this expectation, I use two panel datasets in Western Europe fielded amid the Capitol insurrection. Both analyses show that the expressed support for domestic far-right parties decreased after this autocratization event. I discuss how these findings enrich the literature on autocratization, the far-right, and transnational learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".