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Record W4408058113 · doi:10.1080/02723638.2025.2468071

Hacking suburban social infrastructure: glitch subjects and queer practices of social reproduction

2025· article· en· W4408058113 on OpenAlexafffundabout
Alison L. Bain, Wiley Sharp

Bibliographic record

VenueUrban Geography · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of TorontoYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsQueerReproductionGlitchHackerSociologyGender studiesBiologyComputer securityComputer scienceEcologyTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.318
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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