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Record W2990909165 · doi:10.1111/tesg.12396

Scavenging for LGBTQ2S Public Library Visibility on Vancouver’s Periphery

2019· article· en· W2990909165 on OpenAlexafffundabout
Alison L. Bain, Julie A. Podmore

Bibliographic record

VenueTijdschrift voor Economische en Sociale Geografie · 2019
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsJohn Abbott CollegeYork University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsQueerInsiderInvisibilityPoliticsVisibilitySociologyPerformative utteranceMedia studiesGender studiesPolitical scienceGeographyAestheticsArtLawComputer science

Abstract

fetched live from OpenAlex

Abstract This paper uses mixed methods to scavenge and analyse lesbian, gay, bisexual, trans, queer and two‐spirit (LGBTQ2S) resources and visibility in public library branches on the periphery of the Vancouver city‐region in the suburban municipalities of Surrey, Burnaby, and New Westminster. It argues that in Canadian suburbs, where access to LGBTQ2S community resources are limited, libraries are simultaneously key public sites of engagement for LGBTQ2S people and places where they continue to face dominant socio‐cultural power relations of hetero and cis‐normativity that determine the availability, visibility, and accessibility of queer materials. Scavenging for queerness within virtual and material library spaces, this paper demonstrates that suburban libraries hold the promise of information and equity while also reinforcing LGBTQ2S absence, invisibility, and exclusion. The promise of social inclusion is carried forward by a few insider activist and ally librarians who act as political change agents within library systems and specific branches by creating queer‐friendly collections, spaces, and programming.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.240
Threshold uncertainty score0.483

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0100.004
Scholarly communication0.0080.001
Open science0.0020.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.020
GPT teacher head0.303
Teacher spread0.282 · 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

Citations12
Published2019
Admission routes3
Has abstractyes

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