The 1990s party realignment of U.S. presidential elections: Geographic sorting of counties by blue or red
Bibliographic record
Abstract
Abstract Introduction This paper addresses the issue of partisan polarization in the U.S. in presidential voting at the county level. The literature on the growing partisan sorting and/or polarization is extensive, and controversies are pervasive. This paper focuses on the timing and sequence of changes in partisan vote shares at the county level from 1952 through the 2020 election. Methods We apply confirmatory factor analysis to identify the structures of partisan competition in counties from 1952 to 2020, and multiscale geographically weighted regression (MGWR) to uncover the social processes that influence the growing partisan polarization in the U.S. Results We find that the growing partisan segregation coincides with the beginning of a secular change in the structure of partisan competition at the presidential level among counties that has intensified over the past quarter century. Polarization, segregation of partisans in communities, and a rise in sectionalism are the predominant characteristics of this new alignment. Conclusion This study confirms a secular realignment in the structure of party competition among counties in presidential elections starting in 1996 and solidifying in 2008 when Obama is elected. The segregation of counties by dark shades of red or blue is the unique fingerprint of this new alignment that we call the "Geographic Sort" alignment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".