Fair-Weather Voters: Personality and Vote Switching Intentions
Bibliographic record
Abstract
While numerous explanations for vote-switching have been proposed (e.g. declining rates of partisanship, ideological shifts, partisan ambivalence, change in policy preferences), far less work has examined the personality profile of people more likely to engage in this behaviour. In Study 1, we examined the relationship between both general (i.e. openness, conscientiousness) and antagonistic (i.e. psychopathy, narcissism, Machiavellianism) personality traits and the intent to switch one's vote in a large sample of Canadian citizens, while controlling for several established correlates such as age, income and political interest. Of all personality traits, only individuals higher in openness reported a greater intent to engage in vote switching. Despite our expectations, Machiavellianism, a trait characterized by its strategic nature, was unrelated to vote switching intentions. In Study 2, we addressed several methodological reasons for why antagonistic traits may have been unrelated to vote switching intentions in Study 1 by examining the traits at the facet level and utilizing a new measure of Machiavellianism among a separate sample of Canadian citizens. Here again, we found little evidence for a relationship between antagonistic traits, including Machiavellianism, and vote switching intentions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".