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Record W3183689169 · doi:10.1177/13548565211032377

Online fandom communities as networked counterpublics: LGBTQ+ youths’ perceptions of representation and community climate

2021· article· en· W3183689169 on OpenAlexfundaboutno aff
Lauren B. McInroy, Ian Zapcic, O Beer

Bibliographic record

VenueConvergence The International Journal of Research into New Media Technologies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLesbianTransgenderSexual minorityQueerGender studiesFandomSociologyNarrativeMass mediaPolitical scienceMedia studiesArt

Abstract

fetched live from OpenAlex

Online fandom communities (OFCs) provide lesbian, gay, bisexual, transgender, queer, and other sexual and/or gender minority (LGBTQ+) youth opportunities to access community-generated LGBTQ+ representations—contrasting mass media’s continued deficiencies in depiction of LGBTQ+ people and communities. This study sought to better understand LGBTQ+ adolescents’ and young adults’ (age 14–29) perceptions of OFCs regarding LGBTQ+ representation and community climate. Qualitative content analysis was employed to analyze open-ended survey questions from respondents in the United States and Canada ( n = 3665). Three primary themes emerged: (1) LGBTQ+ mass media narratives remained insufficient but were improving; (2) counternarratives produced within OFCs were even better; however, (3) the climate of OFCs created challenges and limitations, including to the quantity and quality of depictions of diverse LGBTQ+ identities. Findings indicate OFCs may take on simultaneous qualities of networked publics and counterpublics, allowing youth opportunities to contest LGBTQ+ mass media depictions and problematic representations within OFCs.

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)0.003
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.160
GPT teacher head0.467
Teacher spread0.306 · 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 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

Citations27
Published2021
Admission routes2
Has abstractyes

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Same venueConvergence The International Journal of Research into New Media TechnologiesSame topicSocial Media and PoliticsFrench-language works237,207