Unraveling the Radical Flank Effect: The Role of Moderate Organizations in the Face of Radical Flank Violence
Bibliographic record
Abstract
Why do moderate social movement organizations sometimes benefit from or avoid the negative effects of radical flank violence, while in other cases, they suffer strategic setbacks due to such extremism? Scholars have diverged in their conclusions regarding the impact of radical flank actions on more moderate organizations, a phenomenon known as the Radical Flank Effect (RFE). Some argue that radical elements within a movement can inadvertently boost the credibility and support for moderate groups by offering a contrast. Others believe that violence can tarnish the movement’s overall image as extremist, negatively impacting moderates. I propose that these varying conclusions stem partly from a lack of focus on the agency of moderate organizations in managing the extent to which radical factions harm their core interests. Examining the emergence of radical flank violence in the Quebec pro-independence movement during the 1960s and 1970s, this study investigates how and when moderates might enhance their distinction from radical elements and avoid detrimental associations. Relying on in-depth interviews with moderate leaders and archival research, the findings reveal that moderates can achieve this by publicly denouncing violence, avoiding interactions with radicals, and signal to state authorities intent to de-escalate the conflict.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".