Putting their money where their mouth is: The gendered dynamics of central party financial transfers to local election candidates
Bibliographic record
Abstract
Political parties around the world have made widely publicized efforts to improve women’s representation in elected office. While many have investigated these efforts by focusing on gender dynamics during candidate recruitment and selection, party support for women after they are nominated remains somewhat under-analysed. We begin addressing this gap by asking if central party bodies provide women candidates with additional financial support during general election campaigns. Our study leverages population data capturing intraparty financial transfers within three major parties during the 2008 and 2011 Canadian federal elections ( n=1845). The results demonstrate that parties, regardless of ideology, can and do support women candidates with additional campaign funds. However, support from the centre is not always consistent across time or competitive contexts. We conclude that if political parties are sincere in wanting to reduce representational inequities, then consistently providing women candidates with additional financial support is another way of doing so.
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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 | 0.001 | 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".