Third Party Election Spending in Canada and the United Kingdom: A Comparative Analysis
Bibliographic record
Abstract
Both the United Kingdom and Canada recently implemented substantial revisions to their electoral laws, including policy measures designed to regulate third parties. Despite similar policy objectives, the limits placed on third parties vary significantly between the two countries, leaving the potential for significant differences in practice. Using an original dataset, this article undertakes a comparative study of recent national elections in each country (UK: 2005, 2010; Canada: 2004, 2006, 2008) to evaluate policy outcomes. Our analysis finds that while there are differences in both the number of third parties and the range of spending in Canada and the UK, one commonality is apparent: there is no “typical” third party in either country. As anticipated, given differences in spending limits, UK third parties outspent their Canadian counterparts; however, in both countries third parties rarely came close to spending to their limits. Rather, third parties are found to play a limited role, at least as measured via absolute spending.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".