Examining county-level factors of Democratic versus Republican shifts between 2016 and 2020 presidential elections in the U.S.
Bibliographic record
Abstract
This study examines factors influencing county-level Democratic vs. Republican shifting rates and their spatial patterns between the 2016 and 2020 U.S. presidential elections. We developed a flipping-potential measure, a standardized metric for comparing county susceptibility to change (not prediction), based on projected party switching timeframes and observed shifting rates. Shifting-rate analysis identified notable Democratic shifts in Colorado and the northeastern U.S., and significant Republican shifts along the Texas-Mexico border and in Arkansas. Flipping-potential analysis revealed Democratic counties with comparatively higher projected potential to flip red along the Texas-Mexico and Arkansas-Mississippi borders, and Republican counties with higher potential to flip blue in Colorado, Washington, and New York. Multiscale Geographically Weighted Regression (MGWR) analysis determined which variable changes (2016–2020) most influenced these shifts. Results indicated that changes in third-party vote percentages and population density were most influential. Decreases in third-party votes largely benefited Democrats, while increased voter turnout favored Democrats in many counties.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".