Politics of Boundary Consolidation: Income Inequality, Ethnonationalism, and Radical-Right Voting
Bibliographic record
Abstract
Scholars have linked income inequality to the recent success of radical-right parties and movements. Yet research shows that inequality reduces participation among groups likely to support the radical right and promotes support for redistribution, an issue championed by the radical left. This raises questions about why, if at all, inequality matters for radical-right politics. The author reconciles previous arguments by developing a theory that connects these phenomena through the process of boundary consolidation. He argues that inequality generates status threats that prompt exclusionary shifts in national group boundaries. This promotes ethnonationalism, a restrictive conception of national membership and, ultimately, support for the radical right, whose mobilization relies on ethnonationalist appeals. Analyses of time-series cross-sectional data from 38 countries support this theory, revealing that inequality is associated with greater ethnonationalism, with distinct associations by income and ethnicity, and that ethnonationalism strongly predicts radical-right voting. The author thus demonstrates how long-term structural changes are linked to contemporary radical politics and how arguments setting economic and cultural causes of the radical right in opposition are inadequate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".