Bibliographic record
Abstract
Abstract Trumpism seeks to maintain white domination. President Trump's policies aimed to restore white power at a time when it seemed to be in jeopardy. This chapter examines Trump's policy record and its impact on the US Black population, focusing on voting rights, policing, and criminal justice. I also discuss the far right's attack on history curricula and public education, specifically its demonization of Critical Race Theory. These efforts to protect and extend white power are not new. They are based on the principles articulated by the Founding Fathers, who asserted the right of white settlers to control the nation. More recent precedents for Trump's racism include the presidencies of Richard Nixon and Ronald Reagan, who, like Trump, ascended politically by mobilizing white racism. While many have labeled Trumpism a fascist movement, I argue that it is better understood as a precursor to fascism. It represents a continuation of the racist origins and traditions of the United States, where the national oppression of African Americans is core to the operation of capitalism. In closing, I offer a strategic proposal for stopping this reactionary movement and preventing it from developing into a full-fledged fascist movement.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".