Do (Some) Canadian Voters Punish a Prime Minister for Calling a Snap Election?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Are voters willing to punish a prime minister for calling an ‘unnecessary’ snap election for purely opportunistic reasons? This paper examines voters' reactions to the Canadian prime minister's decision to call a snap election in November 2000. The decision provoked limited resentment, and that resentment was strongest among partisans of the opposition parties and among those who follow politics closely. Those who do not keep up with politics, it seems, either did not realize that the election was precipitous or simply did not care. The paper shows that resentment about the election call was a consideration in vote choice, but it was a decisive consideration for a very small group of voters. We estimate that the electoral cost to the incumbent Liberal Party was one percentage point. Some voters are prepared to punish prime ministers for opportunistically calling a snap election, but in this case the electoral penalty was small.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it