Linking Knowledge and Action: Political Science and Campaign Finance Reform
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it