Us versus them: Do the rules of the game encourage negative partisanship?
Bibliographic record
Abstract
Abstract Party identification is a well‐documented force in political behaviour. However, the vast majority of work on partisanship considers only its positive side, rather than recognizing that partisan identities may also have a negative component. Recent work has shown that negative partisanship has important effects, such as reinforcing partisan leanings, directing strategic behaviour and increasing the rate of straight‐ticket voting. This study takes a step back to explore the sources of such orientations, rather than the effects. Specifically, it considers whether the electoral system context contributes to the presence of negative affective orientations towards parties. Using data from the Comparative Study of Electoral Systems, we examine the influence of factors related to electoral system features and consider whether their influence is moderated by voter sophistication. Data reveal significant variation in the rate of negative partisanship across countries, and that these differences are related to the electoral system context in which voters are making decisions. We also find some evidence that these effects are moderated by sophistication. This work adds to our understanding of the role of affect in political behaviour, as well as the impact that country‐level institutional factors can have upon the relationship between voters and parties.
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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 | 0.007 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".