Big change or much ado about nothing?: the impact of Bill C-24 and the Bipartisan Campaign Reform Act on political fundraising in Canada and the United States
Bibliographic record
Abstract
Recent changes to campaign finance laws in Canada and the United States provide researchers with a unique opportunity for comparative studies on the effects of reform on fundraising at the grassroots level. In an effort to contribute to the understanding of these recent reforms the following comparative case study examines the effects of Bill C-24 (2003) and the Bipartisan Campaign Reform Act of 2002 on the campaigns of one Canadian Member of Parliament and one American Congressional Representative. The study suggests that while the impact of the most recent American reforms has been somewhat exaggerated by scholars, changes to campaign finance laws in Canada have caused substantial change at both the national and grassroots level.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.029 | 0.018 |
| Scholarly communication | 0.020 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 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".