Bibliographic record
Abstract
For those who assume that increased regulation of political spending is inevitable in democratic nations, recent developments in U.S. campaign finance law appear puzzling. Is deregulation, exemplified by the U.S. Supreme Court's decision in Citizens United v. FEC, a harbinger of things to come elsewhere or further evidence that the United States remains an anomaly? In this volume, experts on the United States, Canada, Great Britain, Australia, Germany, Sweden, France, and several other European nations explore what deregulation means in the context of political campaigns and demonstrate how such comparisons can inform the study of campaign finance in the U.S. Whereas the contributors do not settle on any single theory of change in campaign finance law or any single perspective on the relationship between changes seen in the U.S. and those in other nations over the past decade, they do concur that the U.S. is rapidly retreating from the types of regulations that defined campaign finance law in most democratic nations during the latter decades of the twentieth century. By tracing and analyzing the recent history of regulation, the contributors shed light on many pressing topics, including the relationship between public opinion and campaign finance law, the role of scandals in inspiring reform, and the changing incentives of political parties, interest groups, and the courts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".