White nationalism, armed culture and state violence in the age of Donald Trump
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| 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 it