All (electoral) politics is local? Candidate's regional roots and vote choice
Bibliographic record
Abstract
Many authors argue that candidates are more popular among voters from their own region. Two potential explanations have been suggested: voters’ identification with their home region, and the representation of regional interests. The information on candidates’ residence can be transmitted to voters in different ways, the most easily accessible way being information printed on the ballot paper. However, most studies on “friends and neighbour voting” use aggregate data. Studies that rely on individual level data usually put respondents in hypothetical situations and confront them with synthetic candidates, reducing their realism. To bridge this gap and to test the effect of providing information on the candidates’ residence, we use data from a survey experiment to analyze voters’ responses to ballot paper information on the regional background of real candidates in the 2014 European election in Germany. We find that voters in an open list PR election are more likely to support regional candidates if ballot paper information on the candidates’ geographic background helps them to do so. The appeal of personal ties is a stronger explanation for vote preference than the one based on regional interests.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".