Citizens’ support for the European Union and participation in European Parliament elections
Bibliographic record
Abstract
The dominant paradigm characterizes European Parliament (EP) elections as second-order national elections. Scholars adhering to this view (for example, Marsh, 2008 ; Reif and Schmitt, 1980 ; Schmitt, 2005 ) not only identify these elections as less important, but also emphasize that low turnout in EP elections is unrelated to citizens’ support for the European Union (EU). In this article, I challenge this latter proposition. Analyzing all EP elections since 1979, I first find that higher macro-level support for EU membership leads to higher turnout. Second, I discover that changes in aggregate EU support directly trigger changes in turnout rates. Third, a multilevel analysis of Eurobarometer data confirms these macro-level trends at the micro level and finds that citizens who consider their country's membership in the EU ‘a good thing’ have a higher likelihood of voting in EP elections than those who reject it. These findings have both empirical and theoretical implications. Empirically, the low turnout in EU elections is directly linked to citizens’ rejection of the EU project. Theoretically, the second-order national election thesis needs to be altered. Turnout in EP elections is driven by not only national-level factors but also citizens’ satisfaction with the EU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".