Urban-Rural Residency, Place Identity, and Affective Polarization in the United States
Bibliographic record
Abstract
The US has seen an increase in affective polarization and negative partisanship over the past few decades. Among other factors, previous work suggests that politically homogeneous social contexts foster increased out-group partisan animosity. Given the concurrent widening urban-rural divide in political affiliation, we hypothesize that rural Republicans and urban Democrats will be more affectively polarized than their respective co-partisans. Using 2020 ANES data (N = 8280), we examine partisan in-group and out-group affect among Republicans and Democrats along the urban-rural spectrum, and by urban-rural place identity. In doing so, we find our expectation to be mostly true with some important caveats. First, rural (and rural-identifying) Republicans are cooler towards Democrats, while city and city-identifying Republicans feel warmer towards the opposing party. However, among Democrats, residential identity inconsistently predicts out-group partisan affect, while urban-rural residency does consistently predict negative out-group partisan affect. Urban-rural residency and place identity do not significantly predict partisan in-group affect among Republicans or Democrats, controlling for other factors. In other words, we find that partisan out-group affect varies by residency and place identity, in line with our expectations. We discuss these results in terms of partisan asymmetry, as well as their implications for mitigating negative partisanship.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".