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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".