The lessons of the hour: the anti-democratic republican MAGA movement and systemic white racism
Bibliographic record
Abstract
Here we examine the resurgence of far-right racial politics in the US through the lens of Donald Trump&s;s “Make America Great Again” (MAGA) movement and its deep ties to systemic white racism. Drawing from historical white backlash patterns, our analysis places Trump&s;s racialized rhetoric and policies within a broader reactionary framework fueled by white demographic anxieties and racial resentment. Trump&s;s political ascendancy has solidified the Republican Party as a “white identity” party, deploying voter suppression, anti-immigrant policies, and right-wing language strategies to reinforce systemic racism while maintaining a façade of societal legitimacy. We examine Trump&s;s impact on electoral politics, mobilization of white nationalism, and enduring threats to multiracial democracy. Despite these far-right political realities, historical and contemporary resistance movements demonstrate pathways of political hope for a truly inclusive and democratic U.S. society.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".