Bibliographic record
Abstract
Virtual influencers are a trending media curiosity, given that they can easily blur racial boundaries, as well as boundaries between authenticity and falsehood. One of the most popular stars of the virtual influencer world is Lil Miquela (@lilmiquela), a virtual avatar of ambiguous ethnicity who follows both popular fashion and politics to boost sponsored brands. This article situates Miquela within the performative ecology of Instagram, blurring the lines of her racial identity and authenticity as an emerging form of commodity activism online. As a 3-D Computer-Generated Image (CGI), Miquela’s racialized design poses questions about the representations of the Instagram category for ‘Black/Brown women,’ which are undermined by the appropriation of mixed-race features in her racially ambiguous design. Miquela is also a virtual influencer, and her creators at the technology and media company Brud label her as an “artificially created robot,” further blurring the boundaries between authenticity and falsehood in Miquela’s posts. These boundaries are further informed by the political economy of influencers on Instagram, which engages with the platform affordances of Instagram, namely the platform’s environment of commodity activism and its neoliberal logic. Pulling from André Brock’s Critical Technocultural Discourse Analysis (CTDA), this article examines how the platform affordances of Instagram and the political economy of influencers have significantly shaped the performative nature of Miquela’s racialized design, as well as her racial identity politics online. This article concludes with the potential empowerment of Black/Brown women in charge of art technology (e.g., Miquela), as well as the demand for government and platform regulations to significantly distinguish virtual influencers like Lil Miquela from human influencers.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".