Negative Advertising and Voter Choice: The Role of Ads in Candidate Selection
Bibliographic record
Abstract
Selecting between two candidates during a campaign is a crucial first step toward political involvement: an individual who does not select a preferred political candidate is unlikely to take political action. Can negative campaign ads help individuals make these electoral choices? Empirical evidence on this topic has been mixed. Some argue that negativity can increase the likelihood of choice. Others show that negativity will decrease the likelihood of choice by turning individuals away from the polls. Integrating theories from social psychology and political science I argue and show that under specific conditions, negativity increases the likelihood that an individual will make a candidate selection. Further, I differentiate between the tone and substance of ads to show that negativity has a unique effect on choice. [Supplementary material is available for this article. Go to the publisher's online edition of Political Communication for the following free supplemental resource(s): Robustness checks. This supplemental appendix establishes the importance of the choice point in individual behavior and considers alternative conceptions of exposure to advertising.]
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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 | 0.005 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".