Continuing Questions in Canadian Political Finance Law: Third Parties and Small Political Parties
Bibliographic record
Abstract
Canadian political finance law has struggled with the question of how to treat small political parties and third parties. The author contends that data gathered by Elections Canada concerning third party expenditures over the last four federal elections provides an opportunity to re-evaluate Harper v. Canada (A.G.), which was decided without a strong evidential foundation on the grounds of a reasonable apprehension of harm. The Ontario Court of Appeal’s decision in Longley v. Canada (A.G.) upholding Canada Elections Act provisions that require political parties to meet vote thresholds to qualify for annual allowances, raises constitutional questions about the role of small political parties. The author questions whether a distinction should be made in law between large and small political parties and whether the fear that third parties may become political parties is a sufficient justification to limit the rights of small political parties.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.037 | 0.050 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".