Artificial Intelligence or a Neoliberal Marketing Scheme?
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it