Information and Voting - How Voters Update Beliefs After Natural Disasters
Bibliographic record
Abstract
A recent body of literature explores the conditions under which natural disasters affect electoral behavior. These studies focus mainly on voter reactions to large rescue and relief programs implemented by governments in response to disasters. While findings show that an effective policy response substantively increases governments’ popularity, previous studies do not focus on how these events affect political behavior with regard to environmental issues. Indeed, in general elections the salience of environmental policies is low. While voters might care about and be aware of environmental issues, these have little influence on the outcome of an election. To overcome these difficulties, we rely on voting behavior in a number of environmental ballot initiatives in Switzerland covering the last 20 years. We show that natural disasters expose certain areas to a treatment that leads to a more climate-sensitive voting behavior through a Bayesian updating of beliefs. Such an effect is only detectable in a short period of time (preceding 12 months) and the effect declines as more time passes by. To account for the fact that the treatment may not be perfectly random (e.g. presence of flood protection construction), we use matching and other techniques to compare municipalities to counterfactuals that share the same risk of being hit by disasters. The design allows us to isolate the causal effect of a “salience boost” on environmental issues and to subsequently observe its effect on a behavioral measure. With this study we contribute to the literature of electoral behavior and democratic accountability.
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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 | 0.003 | 0.021 |
| 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) | 0.006 | 0.001 |
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".