Why Do We Only Get Anime Girl Avatars? Collective White Heteronormative Avatar Design in Live Streams
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".