Are LGBTQ+ Candidates Disadvantaged in Financing Their Campaigns? Evidence from Canadian Federal Elections, 2015–21
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 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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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 | 0.001 |
| 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 it