Bibliographic record
Abstract
One of the surprising outcomes of the social media era of the Internet is its internal contradiction between the endless possibilities for fiction, identity play, performance, and lying, and the profile structure, with its insistence on a single, unified, and quantifiable self. Facebook CEO Mark Zuckerberg insists “you have one identity” while the entire history of Internet culture suggests one, in fact, has many. As a result, the meaning of authenticity becomes a crucial point in determining the future of online life, and in this respect, it represents a contradiction that the world of performance is uniquely familiar with. This project reconsiders virtual performances of authenticity and realness as platform-specific social texts, using performance theory to complicate the idea of univocal self-construction in online life. I present the story of Miquela, a virtual Instagram influencer whose complex creation story and dramatic reveal prompts large and nebulous questions about the nature of authenticity, performance, and self-branding in social-media space. I develop three forms of authenticity that Miquela deploys throughout her career to perform as an influencer and Instagram microcelebrity, and update connections between authenticity, intimacy, and self-image to adapt to late-2010s ways of self-branding and personal storytelling online. Further, I argue these same tools are reflected by corporate brands to further compel audiences to entangle brand identity with their own selves. The tropes of Instagram self-performance are deployed across a spectrum of personal, political, social, and capitalist modes, with authenticity enabling this new height of context collapse.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.015 | 0.022 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".