Guns for Votes: Wedge Politics in the Canadian Multiparty System
Bibliographic record
Abstract
Abstract This article contributes to the emerging literature on wedge issues and on electoral strategies in multiparty electoral systems by studying the implementation and effectiveness of a concerted electoral communication strategy deployed by the Conservative party of Canada around the elimination of the gun registry, in the late 2000s. First, using a quantitative content analysis of the Parliamentary debates from 2006 to 2011, it reveals how the Conservatives exploited the issue in order to create divisions amongst opposing opposition MPs and amongst targeted segments of partisan voters of other national parties. Secondly, using georeferenced pool data from Vote Compass, it finds empirical evidence that the Conservatives’ efforts won them new support in the 2011 federal elections, especially amongst cross-pressured voters and within the ridings that were targeted during the debates. In doing so, the article provides a rare empirical example of wedge politics carried in a multiparty system.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".