Do people want a ‘fairer’ electoral system? An experimental study in four countries
Bibliographic record
Abstract
Abstract When judging how ‘fair’ voting rules are, a fundamental criterion used by both scholars and politicians is their ability or inability to produce proportional results – that is, the extent parties’ seat distribution after the elections accurately reflects their vote shares. How about citizens? Do citizens care about how proportional the outcome is? Or do they judge the outcome solely on the basis of how well (or poorly) their party performed? Taking advantage of a uniquely designed survey experiment, this article investigates the causal effect of proportionality on voter support for voting rules in four countries: Austria, England, Ireland and Sweden. The results show that proportionality drives support for the voting rules not above, but beyond party performance. There is little cross‐country variation, which suggests that proportionality is appreciated in different contexts with little status quo bias. These findings have important implications for our understanding of the causal mechanisms linking electoral rules to voter support.
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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 | 0.012 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".