Non-binary Belongings in Toronto: Problematizing the Gay-friendly City
Bibliographic record
Abstract
This thesis examines the experiences of non-binary transgender people in the city of Toronto through the use of photovoice and semi-structured interviews. It is concerned with the ways such individuals articulate their sense of belonging towards Toronto itself as well as the LGBTQ+ spaces of the city. This thesis argues that the ongoing gentrification of Toronto, the persistence of transphobic discrimination, and the prevalence of right-wing politics in the region marginalize non-binary individuals and challenge their ability to belong in spaces throughout the city. While participants particularly identified LGBTQ+ spaces in Toronto as important sites of belonging, networking, and ontological security, these positive experiences stand in contrast with the number of homo- and transnormativities at play within these sites, further cementing the exclusion of non-binary individuals. Therefore, I argue that the envisioning of non-binary inclusive, transgender-friendly urban spaces is a utopian endeavour as it requires a radical rethinking of social, political, and economic structures and hierarchies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".