Evolution of US electoral coalitions: the case of three presidential elections
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Bibliographic record
Abstract
The article analyzes the basis of electoral support of two principal candidates in the 2024 US presidential elections in comparison with two previous campaigns, in which the Republican Party was represented by Donald Trump. The analysis is based on data from exit polls conducted for CNN. The analysis starts with a discussion of the role of “negative voting” (i.e., when the main motive of voting is against the opponent of preferred candidate) in all three campaigns. It continues with research on the dynamics of voting patterns of basic social and demographic cohorts of voters. The analysis shows that Trump received a solid majority of votes amongst: men, voters aged 45-65, voters without a college degree. He won the 2024 elections by securing majorities in cohorts of voters with an annual income below USD 100 000, in rural areas; and due to a broadening support of African and Latino American voters. Conversely, the Democratic Party had advantages among women, younger age cohorts (though more narrow in 2024 than during earlier campaigns), voters with college degrees, urban dwellers and more affluent people. The advantage in votes by middle-class suburban dwellers in all three cases secured for the winner overall electoral victories. The electoral basis of major US political parties (often referred to as “coalitions”) are complex. The Democratic base includes, on one hand, those cohorts of “underprivileged” voters which historically supported the party as the protector of their interests. However, in 2024 the Democrats lost most of the support of one of these cohorts, i.e., the blue-collar workers. On the other hand, the Democratic base includes liberal and progressive-minded cohorts, most of which are have confidence in life career prospects. The Republican support base includes all the conventional conservative-leaning cohorts. Not all the people belonging to such cohorts are radical or populist, but most of them accept Trump’s leadership style. A new “acquisition” for the party is the majority they obtained among voters without college degrees (mostly blue-collar workers).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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