Leveling the Playing Field? The Role of Public Campaign Funding in Elections
Bibliographic record
Abstract
In a series of First Amendment cases, the U.S. Supreme Court established that government may regulate campaign finance, but not if regulation imposes costs on political speech and the purpose of regulation is to “level the political playing field.” The Court has applied this principle to limit the ways in which governments can provide public campaign funding to candidates in elections. A notable example is the Court's decision to strike down matching funds provisions of public funding programs (Arizona Free Enterprise Club's Freedom Club PAC v. Bennett, 2011). In this paper, we develop a contest-theoretic model of elections in which we analyze the effects of public campaign funding mechanisms, including a simple public option and a public option with matching funds, on program participation, political speech, and election outcomes. We show that a public option with matching funds is equivalent to a simple public option with a lump-sum transfer equal to the maximum level of funding under the matching program; that a public option does not always “level the playing field,” but may make it more uneven and can decrease as well as increase the quantity of political speech by all candidates, depending on the maximum public funding level; and that a public option tends to increase speech in cases where it levels the playing field. Several of the Supreme Court's arguments in Arizona Free Enterprise are discussed in light of our theoretical results.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".