Trump's electoral speeches and his appeal to the American white working class
Bibliographic record
Abstract
This paper contributes to the study of social change by considering boundary work as a dimension of cultural change. Drawing on the computer-assisted qualitative analysis of 73 formal speeches made by Donald Trump during the 2016 electoral campaign, we argue that his political rhetoric, which led to his presidential victory, addressed the white working class's concern with their declining position in the national pecking order. He addressed this group's concern by raising their moral status, that is, by (1) emphatically describing them as hard-working Americans who are victims of globalization; (2) voicing their concerns about 'people above' (professionals, the rich, and politicians); (3) drawing strong moral boundaries toward undocumented immigrants, refugees, and Muslims; (4) presenting African Americans and (legal) Hispanic Americans as workers who also deserve jobs; (5) stressing the role of working-class men as protectors of women and LGBTQ people. This particular case study of the role of boundary work in political rhetoric provides a novel, distinctively sociological approach for capturing dynamics of social change.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".