Bibliographic record
Abstract
Are issue attitudes of rural residents aligned with those of Republicans in theUnited States? Previous research demonstrates an urban-rural divide in issue attitudes whereby rural residents tend to adopt more conservative policy positions and urban residents tend to adopt more liberal ones in the US. In this paper, we investigate whether this notion holds true, or if rural residents are indeed their own unique constituency that carries interests different from what is traditionally “Republican”. We examine canonical issues that are widely discussed in the political discourse and leverage data from the 2020 ANES to compare responses between rural and urban residents, Democrats and Republicans, and the interaction between these factors. In doing so, we find that urban-rural issue differences reflect partisan issue differences - e.g., rural Democrats resemble their urban counterparts and urban Republicans resemble their rural counterparts - rather than rural areas specifically being more Republican. However, we identify certain issues relating to immigration where rural Democrats are more conservative than urban Democrats. These results support the idea that rural America is not always reflective of conservatism and Republicanism. In addition, it points to the role of partisan nationalization in issue stances across the urban-rural spectrum; future scholarship should aim to further illuminate the complexity of this nationalization versus the relevance of place and local considerations in other facets of American politics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".