Replication Data for: Violence Against Politicians, Negative Campaigning, and Public Opinion: Evidence from Poland
Bibliographic record
Abstract
It is commonly viewed that violence against politicians increases support for the victim's party. We revisit this conjecture drawing on evidence from an assassination of an opposition politician in Poland. First, we analyze engagement with Twitter content posted by opposition and government politicians using a difference-in-differences framework. Second, we use a public opinion survey collected in the days around the attack, and compare party preferences of respondents interviewed just before and respondents interviewed just after the attack. Our results reveal decreased support for the victim's (opposition) party relative to support for the government. To explain this finding, we show that the opposition antagonized the public by engaging in negative campaigning against the government over their politician's assassination. Content analysis of tweets and news media confirms that citizens punished the opposition for their negative campaigning after the violence. Tentative evidence suggests that these effects could have had long-run political consequences.
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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 | 0.001 | 0.021 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".