Identifying LGBTQ2S+ candidates: comparing three approaches
Bibliographic record
Abstract
In this research note, we compare three approaches to identifying LGBTQ2S+ candidates: candidate surveys, published lists of LGBTQ2S+ candidates, and systematic searches of each candidate running. Using the case of Canadian federal election candidates for 2019–2021, we show that about 10 percent of out LGBTQ2S+ candidates are missing from published lists. Systematic individual candidate searches are necessary to find these missing candidates. These missing candidates differ systematically from those on published lists, and analyses that do not include them risk underestimating barriers facing LGBTQ2S+ candidates. As work on LGBTQ2S+ candidates expands, we encourage other researchers to adapt to other contexts our procedure of individual candidate searches. Although this approach is time intensive, we argue it is worthwhile both methodologically and normatively.
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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 | 0.070 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".