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Record W4220968405 · doi:10.1177/15274764221080956

Why Do We Only Get Anime Girl Avatars? Collective White Heteronormative Avatar Design in Live Streams

2022· article· en· W4220968405 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTelevision & New Media · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAvatarAnimePopularityGirlMerge (version control)Assemblage (archaeology)SociologyWhite (mutation)MetaverseComputer scienceMedia studiesHuman–computer interactionPsychologySocial psychologyVirtual realityHistory

Abstract

fetched live from OpenAlex

With live streaming rising in popularity, many people stream the creation of 3D avatars However, many of these avatars end up following a similar output: a hyper-feminized anime girl. Why is this? What are the social and technological processes constructing these avatars? To answer these questions, I propose that human (streamer and audience) and non-human (streaming platform and 3D modeling software) participants interact to produce the cultural experience of the live stream, re-producing common heteronormative, cisgendered, and racialized tropes about bodies and desirable avatars. And so, I take as my object of study the interaction that happens when all of these participants merge, forming what I call a white heteronormative assemblage. I argue that this assemblage is collective, relational, and self-reinforcing. Analyzing the relations between human and non-humans participants helps us turn our analytical lens away from media content or streamer motive, and instead toward the restrictive outcomes of such interactions.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.290
Teacher spread0.244 · 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