Wrong place or wrong party? LGBTQ2S+ candidates and district competitiveness
Bibliographic record
Abstract
The sacrificial lambs thesis holds that internal processes lead parties to nominate candidates from marginalized groups in unwinnable districts. This thesis was first developed to explain women's underrepresentation, but it has since been applied to other groups. The case of LGBTQ2S+ candidates presents an opportunity to explore whether the distribution of candidates across parties can account for (some of) the sacrificial lambs pattern. Are LGBTQ2S+ candidates sacrificial lambs because they run in less winnable districts than their straight cisgender (cis) counterparts or because less competitive third parties are more likely to nominate them? We reconceptualize the sacrificial lambs pattern as a gap in district competitiveness. Conceptually, we see this gap as having two components: a within-party component (from differences in where parties nominate members of a marginalized group) and a between-party component (from differences in which parties nominate more members of a marginalized group). We illustrate how to decompose the gap using data on LGBTQ2S+ candidates in Canadian elections, 2015–2021. We construct probability-based measures of district competitiveness and then use Kitigawa-Blinder-Oaxaca decomposition to calculate the within- and between-party components. We find large gaps in district competitiveness in 2019 and 2021, the majority of which is attributable to between-party inequalities. Nonetheless, a substantial portion of this inequality reflects within-party inequalities. Our results suggest that efforts to improve LGBTQ2S+ representation will need to address between-party inequalities in addition to the more traditional focus on within-party inequalities. Our approach could be used to study other groups in other contexts.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".