Bibliographic record
Abstract
Abstract Across the West, there has been a resurgence of ethnic nationalism, populism, and anti-immigrant sentiment—a phenomenon that many commentators have called the “new nationalism.” This book seeks to understand why the bastions of liberalism are proving to be fertile ground for a decidedly illiberal ideology. To do so, it examines three of the most successful exemplars of the new nationalism: Donald Trump in the US, Marine Le Pen in France, and Brexit in the UK. To understand the success of these new nationalists, it looks at the role of white majorities, their cultures, and their histories. Through a careful analysis of the social media campaigns of Trump, Le Pen, and the Brexit campaigners, the book shows how today’s new nationalists are cultivating support from white majorities by drawing from long-standing myths and symbols to construct an image of the nation as an ethnic community. This multidisciplinary approach—combining elements of political science, sociology, history, and communication and media studies—shows how leaders today are updating the historical foundations of ethnic nationalism for the digital age. This analysis helps us see that the success of Trump, Le Pen, and Brexit are only puzzling if we accept the myth that America, France, and Britain are liberal, civic nations. As the book demonstrates, each of these political communities has long been defined by a tradition of ethnic nationalism that continues to shape politics today. In short, the new nationalism is not so new.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".