Who Controls the Purse Strings? A Longitudinal Study of Gender and Donations in Canadian Politics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Gender gaps in voter turnout and electoral representation have narrowed, but other forms of gender inequality remain. We examine gendered differences in donations: who donates and to whom? Donations furnish campaigns with necessary resources, provide voters with cues about candidate viability, and influence which issues politicians prioritize. We exploit an administrative data set to analyze donations to Canadian parties and candidates over a 25-year period. We use an automated classifier to estimate donor gender and then link these data to candidate and party characteristics. Importantly, and in contrast to null effects from research on gender affinity voting, we find women are more likely to donate to women candidates, but women donate less often and in smaller amounts than men. The lack of formal gendered donor networks and the reliance on more informal, male-dominated local connections may influence women donors’ behavior. Change over a quarter century has been modest, and large gender gaps persist.
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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 it