A Blocked Pipeline : Recruitment, Nomination, and Election of Women Candidates in Canadian Federal Elections, 2004-2019
Bibliographic record
Abstract
This dissertation addresses the question of women's descriptive underrepresentation in Canadian politics at the federal level. Previous studies of women's underrepresentation in Canada and elsewhere have largely focused on analysing the results of elections, and studies that do account for earlier factors such as recruitment and candidate selection are limited in their scopes. In this dissertation I analyse women's representation in a holistic manner, accounting for factors from the pre-nomination stage up through election. Conceptually, I approach the path to political office as a three-stage "representation pipeline," comprising candidate emergence, candidate selection, and election. I base my analysis on Elections Canada's records of nomination contests held by federal political parties for the 2004 through 2019 general elections, paired with relevant district-level demographic factors from the Canadian census. I complement this analysis with an original survey of nomination contestants in the 2019 election. I find that women's underrepresentation in Canada is determined chiefly by issues in candidate emergence, rather than by issues in candidate selection or electoral discrimination. Instead, nominations in Canada are in the strong majority of cases acclamations, making candidate emergence and election the only meaningful barriers to women's representation in most cases. Furthermore, women face a small but significant degree of electoral discrimination, with women having slightly lower odds then men of winning election even when controlling for their party's past performance in the district. Finally, I find that urban districts are more conducive to women's representation at all three stages of the representation pipeline.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".