Missing Trans Bodies in The Canadian House Of Commons: Paths To Power For Gender-Diverse Canadians
Bibliographic record
Abstract
Despite substantial public acceptance for Canadian sexual and gender minorities, LGBT people remain on the margins of obtaining equitable federal political representation. In the 2015 election, twenty-one openly LGBT candidates ran for office with just six winning a seat in the House of Commons (HoC). Of these candidates, one identified as openly-transgender. According to John Adams, legislatures should be “[miniature portraits] of the people at large, and … should feel, reason and act like them.” If so, Canada’s parliament lags in achieving LGBT equitable representation, given that LGBT people make up as much as ten percent of the Canadian population yet only hold 1.7 percent of Parliamentary seats. Although LGB people lack suitable federal representation, transgender, transsexual, intersex and gender nonconforming individuals (TTIGs) are entirely disregarded. No TTIG-candidate has ever won federal office. What explains this lack of representation? What are the socio-political barriers barring TTIGs from running (and winning) and what must be done to ensure greater HoC representation? Utilizing media sources and candidate-perspectives, this paper will argue that lacking TTIG representation is due to the multiplicity of supply-side problems combined with lacking demandside solutions for TTIG candidates. Too few run for political office because of societal barriers impeding their candidacies - lacking social acceptance, hate crimes, violence, and isolation, alongside politico-institutional barriers - the electoral system, political parties, and Canadian voters themselves. This paper will examine these barriers and potential interventions (ex. institutional reform) to improve TTIG federal representation. TTIG MPs are needed not only for representation-sake. Breaking the glass ceiling serves as a consciousness-raising function, challenging false social perceptions that justify excluding minority political participation. This paradox of social acceptance yet political exclusion must be rectified through changes to Canadian society that begin in the classroom. These false social perceptions must be rectified through positive, anti-discriminative gender identity and expression curriculum. Without a more accepting and tolerant Canada, it will be challenging to ensure the legislature personifies a miniature portrait of the electorate.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.056 | 0.015 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 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".