Campaign Finance Regulation in North America: An Institutional Perspective
Bibliographic record
Abstract
The regulation of campaign finance is a topic of perennial public concern in North America. The United States has lived through four decades of conflict over contribution and expenditure limits, which has only intensified since the U.S. Supreme Court's decision in Citizens United. Canada has grappled with contribution and expenditure restrictions for decades, though its Supreme Court has taken a more deferential approach. Mexico experienced a spectacularly successful transition to multiparty democracy this century yet faces persistent concerns regarding corruption, agency capture, and powerful party elites. This article compares the campaign finance systems of Canada, Mexico, and the United States, drawing on the institutionalist strain of American election law scholarship. It has four objectives: (1) to provide an overview of the campaign finance laws in each country and the institutions responsible for implementing them; (2) to identify the challenges that each country's campaign finance system faces; (3) to consider what Canada, Mexico, and the U.S. might learn from each other; and (4) to reflect on the broader institutional lessons that might be drawn from their experience. The American model presents the most dysfunctional of the three systems. The Federal Election Commission's bipartisan structure leads to underenforcement of campaign finance rules, while U.S. constitutional law—which rejects equality as a basis for regulation—sharply limits the flow of money into election campaigns. The Mexican and Canadian regulatory models present a sharp contrast, with better functioning institutions that have embraced the goal of promoting a level playing field. Despite the differences, the three North American democracies face some common challenges in ensuring evenhanded implementation. The article concludes with reflections on what these three countries might learn from each other, and the broader institutional lessons for other countries trying to improve their campaign finance institutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".