Keep your Enemies Closer: Strategic Platform Adjustments during U.S. and French Elections
Bibliographic record
Abstract
A key tenet of representative democracy is that politicians' discourse and policies should follow voters' preferences.In the median voter theorem, this outcome emerges as candidates strategically adjust their platform to get closer to their opponent.Despite its importance in political economy, we lack direct tests of this mechanism.In this paper, we show that candidates converge to each other both in ideology and rhetorical complexity.We build a novel dataset including the content of 9,000 primary and general election websites of candidates for the U.S. House of Representatives, 2002-2016, as well as 57,000 campaign manifestos issued by candidates running in the first and second round of French parliamentary and local elections, 1958-2022.We first show that candidates tend to converge to the center of the ideology and complexity scales and to diversify the set of topics they cover, between the first and second round, reflecting the broadening of their electorate.Second, we exploit cases in which the identity of candidates qualified for the second round is quasi-random, by focusing on elections in which they narrowly win their primary (in the U.S.) or narrowly qualify for the runoff (in France).Using a regression discontinuity design, we find that second-round candidates converge to the platform of their actual opponent, as compared to the platform of the runner-up who did not qualify for the last round.We conclude that politicians behave strategically and that the convergence mechanism underlying the median voter theorem is powerful.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".