Bibliographic record
Abstract
Does party ambivalence, that is, simultaneously evaluating positively more than one political party, decrease turnout? The extant literature on this question is limited to the American case, and findings are rather mixed. Using the data from the Comparative Study of Electoral Systems project, this paper provides a first large-scale comparative analysis of the ambivalence-turnout nexus in 46 countries. Based on two different ambivalence measures, I show that party ambivalence is more prevalent in multiparty systems and that a substantial portion of citizens are ambivalent. Moreover, ambivalence, on average, reduces turnout by at least 4.5 percentage points across countries. Importantly, however, this is not the case for every country. Whether ambivalence decreases voter turnout is conditioned by macro-level factors. More specifically, ambivalence tends to dampen turnout in (1) polarized contexts, (2) parliamentary systems, (3) voluntary voting countries, and (4) less fragmented 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".