Are Openly LGBTQ2+ the New Sacrificial Lambs? Campaign Contexts and the Gendered Implications for LGBTQ2+ Candidates
Bibliographic record
Abstract
Abstract Recent increases in the number of openly LGBTQ2+ candidates have not resulted in a corresponding rise in the number of LGBTQ2+ politicians elected to the Canadian House of Commons, reviving the hypothesis of the “sacrificial lamb” candidacies. Drawing upon Lovenduski and Norris’ work on political recruitment, we analyze the backgrounds and experiences of the 172 LGBTQ2+ candidates who ran in the 2015, 2019 and 2021 federal elections in Canada. Our approach is based on the idea that LGBTQ2+ candidacies are the new sacrificial lambs of Canadian politics, although some of them seem less likely to be sacrificed than others. Indeed, we highlight how the electoral opportunities (for example, district competitiveness) afforded to LGBTQ2+ cis men are more likely to result in success than those afforded to LGBTQ2+ cis women or gender minority candidates.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".