Modelling the effect of campaign advertising and contributions on US Presidential elections when differences across states matter
Bibliographic record
Abstract
In a stochastic electoral model of the US Presidential election campaign advertising and contributions are introduced to examine the effect of ads on voters' choices and the influence activists have on candidates' policy positions when differences across states matter. Policy-motiovated activists contribute time and money to influence candidates' policy positions. Candidates use activists' resources to run their ads campaigns and take into account differences across states by running different policy and ad campaigns in each state and at the national level. Voters, identified by their state of residence, their ideal policy and campaign tolerance level and their sociodemographic characteristics, care about candidates' policies relative to their own, about the frequency with which candidates contact them relative to their campaign tolerance level and make voting decision taking into account candidates' valences. State and national ad campaigns give voters a further impetus to vote for candidates. Prior to the election, activists decide on their campaign contributions and candidates announce their national and state policies. In the local Nash equilibrium at the state level, candidates give maximal weight to pivotal voters and minimal to non-pivotal voters and balance the activist and electoral state policy pulls. At the national level, candidates give maximal weight to swing states and minimal weight to non-pivotal states and balance the national activists and electoral pulls. These weights are endogenously determined as they depend on the probability with which voters choose each candidate which depend on the candidates' policies and advertising campaigns and on activists contributions in each state and at the national level. The model gives a theoretical rational for candidates' spending more time and money in pivotal states and for treating voters, activists and states differently in their policies or ad campaigns.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".