How Campaigns Matter in Canada: Priming and Learning as Explanations for the Reform Party's 1993 Campaign Success
Bibliographic record
Abstract
The 1993 Canadian election campaign clearly mattered; the fortunes of the Conservative and Reform parties underwent a fundamental re-orientation during the campaign. Previous research has indicated that Reform's success in 1993 was related to the activation of people who were opposed to the welfare state, but this represents only one dimension of Reform's appeal. This article examines the effect of issues on Reform support during the campaign and considers the actual process by which issues affect party support. While it is sometimes said that a candidate primed or made important certain issues for voters, it may be that some of what is labeled priming is actually something else. This article makes a conceptual and empirical distinction between campaign learning and priming. If voters do not know where a party stands on an issue, they cannot adequately use it in their overall evaluation. The evidence demonstrates that the increased importance of attitudes toward the welfare state was largely a function of the distribution of new information or learning, while the increased importance of cultural questions represented priming.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".