<i>Marshall Lecture 2025:</i> Political Information and Network Effects
Bibliographic record
Abstract
Abstract Why do political campaigns so often yield unexpected results? We address this question by separately estimating the direct effect of a campaign on targeted voters and the indirect effect on others in the same social environment. Partnering with a local NGO during Argentina’s 2023 presidential election, we randomized the distribution of leaflets providing an expert assessment of the likely consequences of certain proposals by the outsider candidate Javier Milei. Exploiting Argentina’s unique sub-precinct election reporting system, we show that the campaign reduced Milei’s support among directly treated voters, as expected, but increased his support among untreated voters in treated precincts, producing a backfiring, net-positive effect for Milei. A pre-registered replication confirmed these opposite-signed effects. Using theory and a survey experiment, we show that the minority of voters who disbelieved the campaign were more motivated to discuss it with peers, convincing them to support Milei. This mobilization effect appears especially likely when campaigns criticize outsider candidates. Our results highlight how campaigns aimed at anti-elite candidates can unintentionally mobilize support for them.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 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".