He Hath a Heart For Harping: Stephen Harper and Election Spending in a Spendthrift Age
Bibliographic record
Abstract
The decision by the Supreme Court of Canada in Harper v. Canada was not favourable to Stephen Harper. In a split decision, the Court held that the latest federal campaign spending limits were acceptable. This paper briefly examines the recent history of election financing laws in Canada and looks briefly at three distinct aspects of the Harper decision: The irony of Stephen Harper’s position in the case, evidential and conceptual problems associated with equating commercial speech with political speech, and an examination of whether financial restraints are of declining importance in an era where political influence is more subtly obtained. The paper argues that the Court needs to critically examine the role and efficacy of advertising in the 21st century, and that governments need to understand political influences other than in simple monetary terms and review policy options in this light.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".