Urban–rural policy disagreement
Bibliographic record
Abstract
Abstract Urban–rural divides are large and growing in many national elections, but the sources of this widening divide are not well understood. Recent research has pointed to policy disagreement as one possible mechanism for this growing divide; if urban and rural residents hold increasingly dissimilar policy preferences, this disagreement could produce ever‐widening urban–rural electoral divides. We investigate this possibility by creating a synthesized dataset of nearly 1000 policy issue questions across 10 distinct Canadian national election studies conducted between 1993 and 2021 ( N = 5.3 million), combined with a measure of the urban or rural character of every federal electoral district. This dataset allows us to measure urban–rural policy disagreement across a much larger range of policy issues and over a much longer time period than has previously been possible. We find strong evidence of urban–rural policy disagreement across a range of issues, and especially in areas of cultural policy, including questions relating to gun control, immigration and Indigenous affairs. We further find strong support for the ‘progressive cities’ hypothesis; in nearly all policy domains, urban residents support more left‐wing positions on policy issues than rural residents. However, we find no evidence these urban–rural policy divides have grown since the 1990s. Urban–rural policy disagreement, while large and meaningful, cannot explain the ever‐widening urban–rural political divide.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".