Understanding people’s choice when they have two votes
Bibliographic record
Abstract
This paper introduces a model of vote choice in mixed-member proportional representation systems where electors cast two votes. Despite the growing popularity of mixed systems around the world, a recent stream of literature suggests that the candidate vote contaminates the list vote, inducing the type of behavior observed under majority rule. We propose a new approach to account for these so-called “contamination” effects, a phenomenon that we define as a causal influence making choices more similar across the vote decisions. Since causality entails a time ordering, we argue that contamination arises only when voters choose sequentially. By making use of new survey questions asking respondents about the timing of vote decisions, we can estimate the magnitude of these contamination effects directly. The model is tested using Bayesian multinomial probit models with survey data from the 2013 federal election in Germany. A key contribution of this paper is to show that contamination effects are present only among voters with lower levels of education, and work primarily from the list vote to the candidate vote. We also test a number of predictions about the determinants of the two vote choices in mixed systems.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".