Linking Knowledge and Action: Political Science and Campaign Finance Reform
Bibliographic record
Abstract
The 2002 enactment of the first major reform of U.S. federal campaign-finance law in a quarter century featured a more substantial engagement of political scientists—through research, public advocacy, and expert testimony—than had been the case in the past. This essay reviews the evolution of research on campaign finance from the early twentieth century to the present, the intellectual tensions between the scholarly and reform communities, the conditions in the 1990s that promoted collaborationamong these groups, and the continuing disagreements over how best to manage the problems associated with money and politics—in the United States and in democracies around the world.He gratefully acknowledges the research assistance of Emily Bailard, a Brookings summer intern from Yale University, and Larissa Davis. Bruce Cain, Anthony Corrado, and Trevor Potter provided valuable commentary as discussants when a version of this paper was presented at the 2002 meeting of the American Political Science Association in Boston. Special thanks to Sarah Binder, Richard F. Fenno, Jr., Charles O. Jones, Sheilah Mann, Norman Ornstein, this journal's editors, and two anonymous referees for their helpful comments. The author would like to note that he alone is responsible for whatever errors of fact and judgment remain.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".