Understanding third-party advertising: An analysis of the 2004, 2006 and 2008 Canadian elections
Bibliographic record
Abstract
For approximately two decades, the federal regulation for third-party election spending was the focus of repeated constitutional debate. However, with the 2004 Supreme Court decision in Harper v. Canada, a relative level of policy stability has been established. This stability permits us to evaluate the performance of spending limits according to the principles of the egalitarian model on which it is based. Using an original data set compiled from third-party election advertising reports from the 2004, 2006 and 2008 federal elections, this article offers the first empirical analysis of this important election policy. A number of observations can be offered. First, third parties are not spending large amounts relative to spending limits. Second, despite legislative changes in 2006 banning all federal party contributions except those from individuals, there appears little strategic action by third parties in spending “around” contribution limits. During this three-election cycle, third parties quite simply did not spend significant amounts. Current third-party spending limits therefore appear to be situated comfortably within the expectations of the egalitarian model, though why third parties of all types spend so little remains in question.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".