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Record W3034847343 · doi:10.1111/gec3.12505

“No queers out there”? Metronormativity and the queer suburban

2020· article· en· W3034847343 on OpenAlexafffund
Julie A. Podmore, Alison L. Bain

Bibliographic record

VenueGeography Compass · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsYork UniversityConcordia UniversityJohn Abbott College
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsQueerScholarshipGender studiesSociologySuburbanizationMetropolitan areaHuman sexualityTransgenderGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

Abstract Despite past projects to “decentre” metronormativity—a societal bias toward queer urban imaginings—in geographical scholarship, attention to suburbia has been limited due, in part, to reliance upon, and reinforcement of, an urban–rural binary that disqualifies the metropolitan periphery. This paper unpacks this binary by reviewing key themes at the intersections of the queer and suburban within the subfields of geographies of sexualities and queer geographies. It begins by outlining the American metronormativities critique and evaluating the claim that the “non‐metropolitan” should be the primary arena for unsettling the queer urban. Four key themes from the Anglo‐American‐Australian literature on the queer suburban are then surveyed: suburbanization processes, suburban relocations, suburban “ways of life,” and suburban home‐making. Having evaluated the current state of the subfield, the paper concludes by pointing to the possibilities of the queer suburban for future urban geography and geographies of LGBTQ+ sexualities scholarship.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.031
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.022
GPT teacher head0.252
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations34
Published2020
Admission routes2
Has abstractyes

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