From Class War to Race War: Historicizing the Devolution from New Deal Populism to “Trumpism”.
Bibliographic record
Abstract
This thesis historicizes the seemingly aberrant case of White working-class support for Donald Trump. Specifically, the debate focuses on three major tropes that recurred throughout Trump's speeches and campaign materials: an ongoing attack on a nebulous group of "special interests," an attendant demand for a return to "law and order," and a celebratory appeal to an undefined "silent majority". Using Ernesto Laclau's theorization of "floating signifiers" to frame my debate, this thesis analyzes campaign materials, polling evidence, and secondary sources to judge how said populist tropes gravitated away from their progressive connotations of class warfare during the latter half of the 20 th century. The following project finds that Trump and his reactionary forebearers used these formerly progressive signifiers to channel post-Civil Rights White backlash towards a conspiratorial "special interest" network of liberal Washington elites that had overlooked a victimized "silent majority" of workers in favor of racially marginalized citizens through an "unfair" tax-and-spend agenda.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".