Voting “Ford” or Against: Understanding Strategic Voting in the 2014 Toronto Municipal Election
Bibliographic record
Abstract
Objective We investigate the phenomenon of municipal‐level strategic voting in a high‐profile mayoral election with a nonpartisan ballot. The rate of strategic voting is calculated, and we investigate whether different types of anti‐candidate attitudes (based on policy or personality) affect strategic behavior. Methods We use survey data from the 2014 Toronto Election Study. Results The estimated rate of strategic voting was 1.3 percent. Among those who did cast a strategic ballot, we find that anti‐candidate attitudes did not affect the likelihood of voting strategically—until the source of the dislike is considered, at which point electors who dislike a candidate on the basis of personality are shown to be more likely to cast their ballots strategically. Conclusions Strategic voting was minimal, and did not affect the election outcome. The type of dislike toward a candidate (either on the basis of policy or personality) affects strategic behavior.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".