An Informed 'No': Voting Behaviour in the Irish Fiscal Compact Referendum
Bibliographic record
Abstract
Informed electoral decisions are key to the well-being of democratic institutions. Citizens gather relevant information not only via their peers but also from the mass media and they do so mostly during electoral campaigns. This paper aims to assess the role played by the Internet in influencing vote choice in the 2012 Irish referendum on the Fiscal Compact. Specifically, we explore the effect of online information on a single policy decision: the yes/no vote in the referendum. We rely on an original dataset from a representative survey of Irish citizens ‐ carried out after the vote on May 31st. In addition to knowing if they used the Internet, we also have information on which websites respondents browsed during the referendum campaign. Thus, we are able to test the impact of pro- and anti-EU news gathered online on voting behaviour. To assess causality, we exploit the natural variation in broadband availability across the Irish territory to instrument online news-gathering. We find evidence that citizens who access political information on the Internet are more likely to reject the Fiscal Compact. However, the effect of the Internet on the referendum is conditional on the type of websites visited by voters and is mediated by their attitudes towards the EU and the national government. Specifically, voters who regularly visit anti-EU blogs and forums are more likely to vote against the Fiscal Compact, whereas visiting less biased websites does not affect voting behaviour. More interestingly, the Internet increases the probability of voting NO only for those voters who support the EU and the national government. This suggests that our results do not simply capture a selection into online news based on political preferences and party affiliation. Our paper contributes to the literature on voting behaviour and EU studies.
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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 | 0.004 | 0.011 |
| 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) | 0.007 | 0.002 |
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".