Policy Preferences Influence Vote Choice When A New Party Emerges: Evidence from the 2017 French Presidential Election
Bibliographic record
Abstract
A common explanation for electoral victories is that the winning candidate adopted issue positions that appealed to voters, implying that citizens’ choices are based on policy preferences. However, it is not straightforward to determine the causal direction between citizens’ issue preferences and their party choice. An alternative possibility, strongly supported by prior research, is that voters adopt the positions of the parties they vote for to rationalize their votes. The 2017 French presidential election offers a unique opportunity to address that question, as it saw the victory of a candidate who was not backed by one of the established parties. Using panel data, we show that policy preferences measured prior to Macron’s emergence as a candidate led voters with a particular bundle of preferences to support him. We conclude that policy preferences clearly do matter to vote choice and that this effect is most visible when a new party emerges.
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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 | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".