Navigating Opposition: Counterprotest Influence on White Supremacist Mobilization and Tactics
Bibliographic record
Abstract
How do white supremacist organizations respond, strategize, and understand counterprotests? With a unique dataset of 2 million chat messages from 2016 to 2020, we argue that white supremacist groups struggle to mitigate the harmful effects of counterprotesters. On the one hand, counterprotesters increase the collective action costs for white supremacists, forcing them to adopt avoidance tactics, such as organizing secretive or less visible rallies. Yet such evasive strategies often hinder efforts to mobilize large numbers of supporters. On the other hand, in an effort to maximize security, white supremacist groups may equip participants with protective gear or weapons. While this approach enhances preparedness, it simultaneously damages their public image by amplifying the movement’s social stigma. Moreover, their perceptions of counterprotester threats play a crucial role in shaping both their tactical choices and broader strategic orientations. Our findings shed light on the interconnectedness of these dynamics, offering new insights into countermovements.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".