Party funding and campaign financing in international perspective
Bibliographic record
Abstract
This volume deals with questions of political party funding and campaign financing, issues which arouse controversy in many parts of the world. How are the central actors in the political arena supposed to gather the funds necessary to operate effectively on behalf of their chosen political ends? And, how may they spend money in furtherance of their political objectives? The aim of this volume, the first in a new series of Columbia University/London University collaborative projects, is to explore these issues in the specific context of a number of national settings. The studies presented here show that financing questions cannot be addressed independent of the constitutional conventions of the country, the nature of the political parties in the country, and the means of access to publication and the media in any given nation. The national studies in this volume reveal a rich diversity in the approach to regulation in Australia, Canada, the European Union, Japan, New Zealand, Quebec, the United Kingdom and the United States. The topicality of the issues considered is reflected in the fact that since the book was first mooted there have been major decisions of the US Supreme Court and the Supreme Court of Canada, as well as an investigation and report by the Electoral Commission in the United Kingdom, all of which have a direct bearing on the legal and policy issues discussed in this book.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".