Sound Allyship? Straight Politicians and LGBQ Representation in Canada
Bibliographic record
Abstract
How do heterosexual politicians who are allies perform LGBQ representation? Through the eyes of Canadian LGBQ parliamentarians, I uncover some heterosexual politicians’ motivations for becoming allies and some of their concrete practices in this regard. I also identify some criticisms voiced by LGBQ parliamentarians in relation to their heterosexual colleagues’ activities of LGBQ representation. Straight politicians become allies for ideological and lived experience reasons. However, beyond best intentions, heterosexual allyship is not without its critics among LGBQ parliamentarians. In conclusion, I contemplate some of the contributions that this article can make to substantive representation and argue for further work to address the voices and experiences of straight parliamentarians on their allyship activities of LGBQ 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.043 | 0.012 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".