“Running the Numbers”: Modes of Microcelebrity Labor in Queer Women’s Self-Representation on Instagram and Vine
Bibliographic record
Abstract
Microcelebrity, as a set of practices contributing to personalized self-branding, has become an increasingly prominent component of self-representation on social media platforms. While “influencers” who have built lucrative followings through microcelebrity give the appearance of having fun without much exertion, recent studies have uncovered multiple forms of labor involved in their practices of cultural production. In addition, scholars analyzing lesbian, gay, bisexual, transgender, and queer (LGBTQ) influencers highlight a tension between labor in service of self-commodification and the representation of sexual minorities. This article examines the microcelebrity labor of everyday queer women who aim to increase their social and economic capital by interweaving personal self-representations with entrepreneurial endeavors on Instagram and Vine. Through a close analysis of these platforms’ markets, governance, and infrastructures alongside interviews with queer female users of each platform, attention is given to both platform influences and participants’ experiences of promoting their jobs, side-gigs, hobbies, or passions alongside the rest of their lives. Findings identify three modes of labor specific to participants’ efforts to build a following: (1) intimate affective labor expended in sharing and managing personal disclosures; (2) developmental aesthetic labor as the acquisition and practice of technical skills and bodily displays to achieve a desired appearance or performance; (3) aspiring relational labor in attempts to forge relationships with established influencers or celebrities. Sexual identity was pivotal across these modes of labor, as it enhanced intimacy with followers, provided a niche audience for self-branding, conveyed authenticity through self-revelation, and established a common ground for forging relationships.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".