White nationalism, armed culture and state violence in the age of Donald Trump
Bibliographic record
Abstract
With the election of Donald Trump to the presidency of the United States, the discourse of an authoritarianism and the echoes of a fascist past have moved from the margins to the center of American politics. A culture of war buttressed by the forces of white supremacy and militarization has been unleashed in a series of policies designed to return the United States to a history in which the public sphere was largely white and Christian, and the economy and the state were governed by a ruling corporate elite. Militarization and a war culture have become normalized in the United States and this article explores the ways in which a neo-fascism has emerged that furthers war not only abroad but also at home, especially with regards to the ongoing assaults waged by the state against Muslims, immigrants, women’s reproductive rights, and poor minorities of class and color. Against the rise of neo--fascism, this article advances the idea that education is central to any viable notion of politics, that progressives need to develop a more unified notion of the political in order to overcome the splintering nature of single issue movements, and that it is important to develop a broad-based social and political formation based on the promise of a radical democracy to come.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| 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".