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. 1 Meghean Keneally, "Donald Trump Captures Presidency in Historic and Stunning Upset of Hillary Clinto n." ABC News, November 9, 2016.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| 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 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".