Banking on victory? Gender, campaign spending, and candidate nomination outcomes in Canada
Bibliographic record
Abstract
While candidate spending is a well-established predictor of electoral success in general elections, much less is known about the role of spending in intraparty elections. Drawing on a unique dataset of over 600 nomination contestants in Canada’s 2019 and 2021 federal elections, this study explores whether dynamics from the interparty arena also apply to the intraparty arena. We find that nomination contestant spending is positively associated with winning a party’s nomination. Contrary to expectations, however, we find no significant gender gap in total fundraising or spending: men and women raise and spend similar amounts. Nonetheless, we uncover consistent evidence of a gender gap in nomination fundraising effort, revealing that women candidates need twice as many donors to achieve the same fundraising results as men. Multivariate analyses confirm these patterns and further reinforce the notion that financial resources are a key determinant of political success not just in general elections but also in the earlier, intraparty, stages of candidate selection.
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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) | 0.000 | 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".