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Record W3093517347 · doi:10.15273/jue.v10i2.10351

“There’s A Little Bit of That Magic Where I’m Becoming Something Else”: LGBT+ Furry Identity Formation and Belonging Online

2020· article· en· W3093517347 on OpenAlexvenueno aff
Mary Heinz

Bibliographic record

VenueJournal for Undergraduate Ethnography · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsnot available
Fundersnot available
KeywordsFandomNormativeSubculture (biology)SociologyTransgenderFeelingJudgementIdentity (music)QueerGender studiesSocial psychologyPsychologyMedia studiesAestheticsEpistemologyArt

Abstract

fetched live from OpenAlex

Active and open identi cation with animals and the creation of anthropomorphic (or zoomorphic) fursonas have become infamous on the internet, yet published research on the subculture is lacking. This ethnographic study explores this under-examined subculture by considering how individuals who identify as LGBT+ and as furry nd and experience community online in ways that contribute to feelings of belonging, inclusion, and overall well-being. Through a series of semi-structured interviews, it was found that participating LGBT+ furries experienced an increase in self-reported emotional well-being when allowed to engage with online furry fandom. Given that furry identity is inherently non-normative, the fandom becomes an accepting space for other non-normative identities, like LGBT+ identities. By creating accepting online communities, those without access to supportive communities in their o -line lives can learn about and explore non-normative identity without judgement. These spaces may allow for the accumulation of multiple non-normative identities, all of which are in relative harmony within online furry fandom, which serves as a “catch-all” identity. Existing within this space had positive a ects on the well-being of the participants.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.251
Threshold uncertainty score0.704

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.109
GPT teacher head0.367
Teacher spread0.258 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

Explore more

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