What Are the Consequences of Snap Elections on Citizens’ Voting Behavior?
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
In some democracies, the ruling party can strategically call a ‘snap’ (or ‘early’) election before the end of its mandate in order to maximise its chances of re-election. Little is known on the consequences of calling such an election. In this article, we contribute to this literature by analysing whether snap elections affect citizens’ voting behaviour. Does being angry at the decision of the incumbent government have an impact on citizens’ decision to vote or not to vote and/or their vote choice calculus? To answer these questions, we make use of two different and independently conducted surveys in Canada during a snap election. We do not find evidence that calling an early election reduces citizens’ likelihood to vote. However, when they do decide to vote, citizens that resent the decision to call an early election are substantially more likely to punish the incumbent government.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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