A Model of Interest Group Influence and Campaign Advertising
Bibliographic record
Abstract
We analyze a citizen–candidate model of elections between an incumbent and challenger to investigate the logic of interest group influence on election outcomes through campaign advertising. Whereas the incumbent’s position is known to voters, the challenger is relatively unknown, and groups may allocate spending (either directly through independent expenditures or indirectly through campaign donations) to advertise the challenger’s position. We prove that equilibria can feature either positive or negative advertising, but not both at the same time: ex ante evaluations of the challenger by the median voter determine which kind of advertising will arise. In a positive advertising equilibrium, only challengers located in a centrally located spending interval are advertised and win, while in a negative advertising equilibrium, challengers who are too extreme are targeted and lose. The analysis sheds light on the determinants of political advertising and voter beliefs, and it emphasizes their endogeneity with respect to the parameters of the model, e.g., the incumbent’s location, prior beliefs of voters about the challenger’s location, and the effectiveness of advertising technology. Moreover, it illuminates the preconditions for positive and negative advertising, and indicates circumstances in which one tactic is more likely to be employed than the other.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".