Are LGBTQ+ Candidates Disadvantaged in Financing Their Campaigns? Evidence from Canadian Federal Elections, 2015–21
Bibliographic record
Abstract
Abstract LGBTQ+ people remain underrepresented in politics, leading scholars to examine a variety of barriers to office. Based on work on women in politics, this paper focuses on one possible barrier: political finance. Is there a political financing gap between straight cisgender and LGBTQ+ candidates? Are there inequalities among LGBTQ+ candidates? If so, what explains them? This article explores these questions by combining a dataset of out LGBTQ+ candidates in the 2015–21 federal elections with political donations data from Elections Canada. When we examine bivariate financing gaps, we find LGBTQ+ candidates receive less money than their straight cisgender counterparts. These gaps are gendered: queer cisgender women, transgender, and nonbinary candidates receive the least money. When we adjust for other variables, we still find LGBTQ+ candidates in the Conservative Party and transgender and nonbinary candidates across parties receive less money. This article contributes to work on gender and identity in campaign finance and LGBTQ+ representation.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".