Hacking suburban social infrastructure: glitch subjects and queer practices of social reproduction
Bibliographic record
Abstract
Infrastructure – material and social – enables urban life for some people and not others. Unevenly distributed between centers and peripheries, it affords differential capacities for action and shapes regimes of social reproductive labor. This paper explores Toronto’s queer suburbanisms, foregrounding the lives of LGBTQ+ suburbanites who have been epistemically erased by urban geographies that discount sexuality and geographies of sexuality that overlook the suburban. It inductively analyzes 192 images from 19 photo-elicitation interviews conducted in the peripheral municipalities of Ajax, Markham, Mississauga, and Brampton in Canada’s largest city-region, Toronto. It shifts attention away from object-centric notions of social infrastructure to prioritize its public social reproductive dimensions and affordances. In turn, it argues that LGBTQ+ suburbanites can be “glitch” subjects that “hack” suburban social infrastructure of homes, public parks, social venues, and public and private transportation, to afford the capacity for queer and trans public life to persist. Broader coalitions are still needed to unbuild cisheteronormative infrastructure and transform city-regional infrastructural landscapes. These quiet political gestures, however small, are nevertheless meaningful attempts to imagine yet-to-be-built suburban worlds.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.040 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".