Bibliographic record
Abstract
This article analyzes the conditions under which ethnic minorities intensify or moderate their protest behavior. While this question has been previously asked, we find that prior studies tend to generalize explanations across a varied set of ethnic groups and assume that causal conditions can independently explain whether groups are more or less mobilized. By contrast, this study employs a technique – fuzzy-set analysis – that is geared toward matching comparable groups to specific analytical configurations of causal factors to explain the choice for strong and weak protest. The analysis draws on a sample of 29 ethnic minorities in Europe and uses three group and two contextual conditions inspired by Gurr’s ethnopolitical conflict model to understand why some ethnic minorities protest more frequently than others. We find that two group-related factors have the strongest claim to being generalizable: while territorial concentration is a necessary condition for strong protest, national pride is a necessary condition for weak protest. The contextual factors of level of democracy and ethnic fractionalization, which are often emphasized in the literature, and the perceived political discrimination of a group, are neither necessary nor individually sufficient conditions for either strong or weak protest. Hence, they help understanding some cases, but not all, and only in combination with other conditions. Such causal complexity, inherent in the phenomenon of ethnic protest, underscores the need for a case-sensitive, yet comparative, approach.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".