The Forty-Year War on Money in Politics: Watergate, FECA, and the Future of Campaign Finance Reform
Bibliographic record
Abstract
This article examines the 40 year history of the post-Watergate campaign finance reforms. Since Watergate, federal campaign finance law has been based on a model of low contribution limits and unlimited expenditures. That long experience provides sufficient evidence to ask and answer a fundamental question: Are we better off today than we were before the Watergate era campaign finance reforms? The thesis of this article is that the answer to that question is no. In fact, in many respects, the current system is worse than that which prevailed before Watergate. This article concludes that contrary to the polarizing rhetoric that surrounds the national debate over campaign finance law, the historical record indicates that both reformers and their opponents offer reasonable policy alternatives to the dysfunctional system that prevails today. For example, twentieth-century political history at the federal level and ongoing experience at the state level demonstrate that a deregulated campaign finance system does not lead inevitably or necessarily to plutocracy. At the same rate, however, Canada’s experience with expenditure caps over the last 40 years shows that robust political debate and high levels of incumbent turnover are possible even within a comprehensively regulated campaign finance environment. Thus, the historical record makes clear that either approach — comprehensive regulation or sweeping deregulation — is preferable to the hybrid campaign finance system that governs American elections today.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".