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Record W7125171736 · doi:10.60808/ry6p-3151

Rural Geographies, Queer Youth and Metronormativity

2025· article· W7125171736 on OpenAlexaboutno aff
Fenton Litwiller

Bibliographic record

VenueVCU Scholars Compass (Virginia Commonwealth University) · 2025
Typearticle
Language
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsNarrativeQueerWhite (mutation)Power (physics)IntersectionalityNarrative inquiryTransgenderFocus groupRural area

Abstract

fetched live from OpenAlex

In 1995 Weston articulated an oft recited story where queers move from persecutory rural geographies to the more accepting city. While Weston interrogated this spatial binary and demonstrated failed promise of the metropolis to deliver, particularly across intersections of gender, the urban flight narrative persists. Jack Halberstam (2005) used the term ‘metronormativity’ to illustrate the power of this narrative that influences a research focus on urban queers. This study contributes insights on how queer youth who live in remote Canadian geographies navigate and express gender and sexuality. Eight participants (12 – 24) attended an informal educational space, a genderplay workshop, and were interviewed onsite. It is within white settler heteropatriarchy that youth in this study negotiated binary gender, and many youths did not claim an identity. While masculine youth felt that the isolation contributed to a neighbourly acceptance, trans feminine youth who could not pass articulated their hometown as dangerous. The workshop results demonstrate the need for educators to move beyond accommodations approaches, where small changes are made for individual youth who are recognizably out, and to contend with their own cisheteronormativity and create cultural change and safer schools.

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.400
Threshold uncertainty score0.794

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0200.019
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.018
GPT teacher head0.286
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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