A Review of the Popular and Scholarly Accounts of Donald Trump’s White Working-Class Support in the 2016 US Presidential Election
Bibliographic record
Abstract
Popular and scholarly accounts of Trump’s ascendency to the presidency of the United States on the part of the American white working-class use different variables to define the sociodemographic group because there is no “working-class White” variable available in benchmark datasets for researchers to code. To address this need, the Author ran a multinomial regression to assess whether income, education and racial identity predict working-class membership among white Americans, finding that income and education are statistically significant predictors of working-class whiteness, while racial identity is not. Arriving at a robust definition of “white working-class” in light of these findings, the paper next turns to a review of the extant literature. By retrieving studies from searches of computerised databases, hand searches and authoritative texts, the review critically surmises the explanatory accounts of Trump’s victory. Discussion of the findings from the review is presented in three principal sections. The first section explains how working-class White communities, crippled by a dearth of social and geographic mobility, have been “left behind” by the political elites. The second section examines how white Americans, whose dominant group position is threatened by demographic change, voted for Trump because of resonance between his populist rhetoric and their latent “racist” attitudes. The third and final section explores the implications of a changing America for native-born whites, and how America’s increasing ethnoracial diversity is eroding relations between its dominant and nondominant groups. The Author surmises by arguing that these explanatory accounts must be understood in the context of this new empirical approximation of “working-class White”.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".