Bibliographic record
Abstract
Abstract Politics is increasingly negative, especially surrounding elections, raising concerns about mass disengagement and democratic backsliding. Despite these worries, the literature on how negativity in voting affects democratic attitudes and voting intentions is riddled with ambiguous and contradictory results. We argue that this may partly be due to the failure to distinguish between two types of negativity in voting: (a) understanding voting as a way to act against a specific party, politician, or policy (negative meanings of voting), and (b) opposition to voting itself (an anti-voting orientation). Contrasting these to the classical conception of voting to support a party, politician, or policy (positive meanings of voting), we conceptualize these constructs and validate their measurement in twelve countries, differing in geography, political systems, and levels of democracy ( N = 23,828). We arrive at two main conclusions. First, positive and negative meanings of voting are complementary and compatible attitudes. Modeling positive and negative voting separately rather than relative to each other shows a more nuanced picture of negative voting than previous work. Second, negative voting and anti-voting orientation are distinct types of negativity that relate differently to classical conceptions of voting and democratic attitudes. The first signals political dissatisfaction but belief in the electoral process. The second corresponds to such a disillusionment about voting that it inhibits dissatisfaction with democracy. As such, this distinction highlights the multifaceted nature of political negativity from a citizen perspective and helps clarify the relationship between negativity and democracy.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".