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Record W2591096331

The Forty-Year War on Money in Politics: Watergate, FECA, and the Future of Campaign Finance Reform

2016· article· en· W2591096331 on OpenAlexaboutno aff
Anthony Gaughan

Bibliographic record

VenueThe Knowledge Bank (The Ohio State University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsCampaign financePoliticsRhetoricState (computer science)Political sciencePolitical economyDeregulationFinancePublic administrationEconomicsLawMarket economy
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.492
Threshold uncertainty score0.978

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0120.026
Scholarly communication0.0120.005
Open science0.0010.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.008
GPT teacher head0.212
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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