Incumbency and Competitiveness in City Council Elections: How Accurate Are Voter Perceptions?
Bibliographic record
Abstract
Abstract Incumbent city councillors have an almost insurmountable advantage in Canadian municipal elections. This article aims to improve our understanding of the municipal incumbency advantage by considering the ability of electors to correctly identify the two most competitive candidates in one's ward and the factors associated with being able to do so. Using survey data from the Canadian Municipal Election Study (CMES), we consider the case of the 2018 elections in Mississauga, a city with typically high rates of incumbent re-election. Survey respondents were asked to identify the two most competitive candidates in their local ward races. We find that comparatively few electors are able to recognize which challenger serves as the strongest threat to a sitting councillor, a finding that suggests that coordination problems may help to contribute to high rates of incumbent success. We identify several individual-level and ward-level correlates of correctly identifying the first-place and second-place finishers. We do note, however, that there is a significant amount of variation among the thousands of municipalities in Canada, so findings from this case should be tested in other settings, including larger or smaller cities where levels of information might be different.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".