Bibliographic record
Abstract
Abstract This chapter discusses the theoretical foundations of campaign finance regulation with a particular focus on the central concepts—political equality, liberty, corruption, participation, representation, free speech, accountability, political influence, and democracy—that have animated scholarly debates. The chapter begins by outlining the key Supreme Court decisions on the constitutionality of campaign finance measures and the central concepts that have emerged from these cases. It then turns to the classic debate between libertarian and egalitarian approaches to campaign finance regulation. It also traces the egalitarian and libertarian strands in the Supreme Court’s decisions. The chapter subsequently analyzes the concept of corruption. It sets out various categorizations of corruption and addresses why corruption is wrong. The chapter then turns to the relationship between campaign finance and democracy, exploring various issues including political inequality, legislative skew, plutocracy, participation, and representation. Finally, the chapter considers the First Amendment and its application to campaign speech and elections. It canvasses debates on various topics, including money as speech, disclosure, electoral exceptionalism, electoral integrity, and the right of participation. The conclusion outlines two avenues of ongoing research: the challenges of social media and judicial deregulation, respectively, which have broad implications for campaign finance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".