Party influence where predispositions are strong and party identification is weak: Assessing citizens’ reactions to party cues on regional nationalism in Spain
Bibliographic record
Abstract
I show that parties’ positions on issues that are rooted in identity influence people’s opinions even if they lack a party identification. When exposed to competing party positions, citizens adjust their issue opinions to make them more consistent with their preferred party’s position even if they do not identify with that party. In two experiments conducted in Spain, I consider how citizens react to party cues on regional nationalism. Study 1, a laboratory experiment in Catalonia, shows that, when exposed to party cues on nationalism, citizens change their issue opinions in the expected direction but only weakly change their party evaluations. Study 2, a survey experiment in Galicia, shows that party cue effects only occur when participants are exposed to competing cues from their preferred party and from a disliked party. Parties thus influence opinions when they adopt contrasting positions even on issues that are rooted in identity.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".