Asymmetries in urban, suburban, and rural place-based resentment
Bibliographic record
Abstract
In many advanced economies, citizens are deeply politically divided on the basis of where they live, and there is growing concern that these divisions are reinforced by place-based resentment — hostility toward place-based out-groups that are perceived to be unfairly advantaged. Drawing on two large-scale surveys of the Canadian public, this paper investigates the size and socio-demographic correlates of place-based resentment in urban, suburban, and rural areas. First, we examine how place resentment varies across all possible combinations of urban, suburban, and rural in-groups and out-groups. Second, we explore if high-resentment individuals in urban, suburban, and rural areas share similar socio-demographic and political characteristics. We then turn to an examination of political implications, investigating how citizens’ satisfaction with their elected representatives, and positions on contentious and important policy issues, relate to place resentment. We find that place-based resentment is highly asymmetric: resentment is strongest among rural residents regardless of the target (suburban or urban) of their resentment, whereas urban and suburban residents tend to resent each other more than either resents rural areas. We also find substantial asymmetries in the correlates and political implications of place resentment. High-resentment rural residents tend to be older, more conservative in their attitudes, and less satisfied with democracy, while high-resentment urban resenters are generally younger, higher in income and education, and more progressive in their attitudes and satisfied with democracy. Our findings suggest that place resentment is an important and politically consequential phenomenon across all place types, but that the character of this resentment differs in rural, suburban, and urban places.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".