The Great Global Divider? A Comparison of Urban-Rural Partisan Polarization in Western Democracies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study is the first to measure urban-rural electoral divides in a way that facilitates comparisons beyond majoritarian democracies of the UK and North America. Based on national election results at the lowest available geographic level in fifteen countries covering roughly five decades, we present a measure for each election and political party, enabling comparisons over time and between countries with different electoral and party systems. We show that long-term increases in urban-rural divides have been most pronounced in the US, the UK, and Canada, but these divides have also emerged in several European multiparty systems in recent decades, largely because of growing smaller parties with predominantly urban or rural support. Overall urban-rural electoral divides remain lower in these systems due to continued presence of mainstream parties with geographically diverse support. Our contribution paves the way for a comparative research agenda on causes and consequences of urban-rural electoral polarization.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it