The rural-urban cleavage in US presidential elections: Stability and sudden change
Bibliographic record
Abstract
Recent elections in the United States are characterized by a strong urban-rural divide, with rural voters being more likely to vote for the Republican Party and Democratic support concentrated in large urban centers. While much attention has been given to the sources of Republican support among rural voters, less is known about how this divide has emerged over time. Using data from the American National Election Studies (ANES), the Cooperative Election Study (CES), and the General Social Survey (GSS), we trace longitudinal trends in the association between living in rural areas and voting in US presidential elections. Our results show that the rural-urban divide was stable for an extended period of time but suddenly became more pronounced in the 2016 and 2020 elections. Comparative analysis reveals that this cleavage now surpasses gender and income divisions in importance, though it remains weaker than race and religious cleavages. We also show that this sudden strengthening of the rural-urban divide is driven by both rural Democrats switching to the Republican Party and urban Republicans switching to supporting Democratic candidates.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".