Influences on the Number and Gender of Candidates in Canadian Local Elections
Bibliographic record
Abstract
Abstract We explore influences on the number of candidates, and female candidates in particular, who contest mayoral elections in Canada. We draw on an original cross-national data set of election results from mayoral elections in Canada's 100 largest cities between 2006 and 2017. An average of 4.96 candidates contested mayoral elections in this period, and 16 per cent of all candidates were women. Density and mayoral prestige were related to higher numbers of candidates; in contrast, incumbent candidates and the availability of other elected positions were related to lower numbers. Notably, the presence of a female incumbent was related to higher numbers of women running for the position of mayor; in contrast, higher mayoral salaries were associated with an increase in the number of male but not female candidates. This analysis enhances our understanding of the factors underlying contested local elections, as well as the factors that appear to facilitate women contesting local elections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| 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 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".