What is old is new again: The deep roots of ethnic nationalism in the digital age
Bibliographic record
Abstract
Abstract In this article, we elaborate on the central themes of our recent book, The New Nationalism in America and Beyond (OUP, 2022), before responding to the comments and criticisms of several esteemed colleagues (Phil Gorski, Cynthia Miller‐Idriss, and Sophie Duchesne). In sum, our book argues that the relative success of right‐wing populists among the white majorities of the West – including Donald Trump in the US, Marine Le Pen in France, and the Brexit Campaigners in the UK – is partly due to the way in which they draw upon long‐established ethnic nationalist myths and symbols in their political communication. By adapting this cultural content to the contemporary context, these elites are ensuring that their messaging resonates among their target populations. In making this case, our book seeks to demonstrate the value of taking culture seriously in the analysis of the so‐called ‘new nationalism.’
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".