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Record W3014715550 · doi:10.1080/14733285.2020.1745755

More-than-safety: co-creating resourcefulness and conviviality in suburban LGBTQ2S youth out-of-school spaces

2020· article· en· W3014715550 on OpenAlexafffundabout
Alison L. Bain, Julie A. Podmore

Bibliographic record

VenueChildren s Geographies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Education Studies and Reforms
Canadian institutionsJohn Abbott CollegeYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSociology

Abstract

fetched live from OpenAlex

Between the sub-disciplines of children’s geographies and geographies of sexualities lie the spatialities of children’s and youth’s sexualities, an understudied subfield especially in non-metropolitan contexts. This paper examines the co-creation strategies of three LGBTQ2S out-of-school youth programmes in the Canadian peripheral municipality of Surrey, British Columbia. It argues that larger youth populations, a circumscribed hetero-temporality, and limited spatial resources necessitate attention to specific modes of co-creating out-of-school spaces for suburban LGBTQ2S youth (aged 15–24). The paper examines the practices of adaptive spatial co-creation with suburban LGBTQ2S youth within the fragmented local suburban governance landscape of community organizations. In contrast with the public visibility stressed within adult urban gay identity politics, a goal of ‘more-than-safety’ is primarily achieved for suburban LGBTQ2S youth through privacy, invisibility, and boundary work that results in weak integration, a lack of collaboration, and the re-bounding of identity parcels across suburbia’s extensive geography.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.225

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 studies0.0120.010
Scholarly communication0.0050.002
Open science0.0010.010
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 head0.031
GPT teacher head0.297
Teacher spread0.266 · 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

Citations22
Published2020
Admission routes3
Has abstractyes

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