Population Size and Incumbency in Canadian Municipal Elections: Two Essays
Bibliographic record
Abstract
In this thesis, I measure the relationship between the electoral success of municipal incumbents and municipal population size in Canada. I first ask how municipal incumbent success rates vary by municipal population size, and discover that acclamations drive overall population-size based trends of municipal incumbent success in Canada. Using an original dataset and a novel modeling approach that accounts for acclaimed incumbents, I find that municipal incumbent success rates generally fall as municipal population size increases. Furthermore, this relationship is particularly strong in Quebec. After excluding acclamations from the analysis, incumbent success rates increase as population size increases. Thus, voters in large municipalities favour incumbents when compared to their counterparts in smaller municipalities. To further investigate this trend, I then ask how the strength of an incumbency cue changes depending on population size, and find that incumbency cues have a stronger effect in larger municipalities. Taken together, these findings reveal that size-related patterns municipal incumbency in Canada are likely dependent on how voters from different sized municipalities process political information and view incumbent candidates.
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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 | 0.000 | 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) | 0.160 | 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".