The Missing Metros: The Voting Patterns and Political Influence of Metropolitan Regions in U.S. Presidential Elections, 1952-2012
Bibliographic record
Abstract
For scholars of urban politics, the 2012 presidential elections were significant in a number of different respects. First, it was striking (though not unusual) that in the course of the campaign the candidates rarely engaged with urban issues such as affordable housing, mass public transit, or urban environmental sustainability (see Baker, 2012). Secondly, once the results of the election were published the increasing polarization of urban-rural voting patterns revealed a country intensely divided. Maps of election results by county published since 2000 show an increasingly stable trend of blue metropolitan regions, even in the hearts of deeply red states (in 2012 notable examples included Austin, Dallas, Houston, San Antonio, Atlanta, Charleston, and New Orleans, among others). This pattern of place-based political polarization and lack of meaningful engagement with metropolitan issues is a unique and increasingly stable feature of American presidential contests. However, it also raises important and troubling questions for political strategists and policymakers alike: What does this pattern mean for future election cycles? And, how long can candidates afford to ignore metropolitan regions?This paper takes a first step towards answering these questions by exploring long-term voting patterns of counties within the 100 largest metro regions in America. It asks: How have voting patterns changed over time depending on location within metro regions? While central cities have long been progressive bastions (even in conservative states) their surrounding suburban regions have tended to be, and hence vote, more conservative (even in more liberal states). However, there is evidence that in many metro regions historically reliably conservative suburban counties have “flipped” in their voting behavior to become supporters of Democratic candidates. While this pattern has been observed, to date few studies have attempted to specifically track its magnitude, its causes, and explore its implications. This paper aims to focus on these issues to identify the extent of suburban “flipping” and pinpoint important tipping points in time and relative to a range of demographic, socioeconomic and market data. The analysis focuses on presidential elections between 1952 and 2012. This data may be useful for future studies that focus on the factors that correlate with voting flips and devise a hypothesis to single out counties that are vulnerable to flips in future elections.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".