Explaining the Trump Vote: The Effect of Racist Resentment and Anti-Immigrant Sentiments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT The campaign leading to the 2016 US presidential election included a number of unconventional forms of campaign rhetoric. In earlier analyses, it was claimed that the Trump victory could be seen as a form of protest voting. This article analyzes the determinants of voters’ choices to investigate the validity of this claim. Based on a sample of the 2016 Cooperative Congressional Election Survey, our analyses suggest that a Trump vote cannot be explained by a lack of trust in politics or low levels of satisfaction with democracy, as would be assumed given the extant literature on protest voting. However, indicators of racist resentment and anti-immigrant sentiments proved to be important determinants of a Trump vote—even when controlling for more traditional vote-choice determinants. Despite ongoing discussion about the empirical validity of racist resentment and anti-immigrant sentiments, both concepts proved to be roughly equally powerful in explaining a Trump vote.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it