Birkin Bags and Beautiful People: How Fashion, Capital, and Fame Collide
Bibliographic record
Abstract
Web-based and mobile platforms such as Instagram have given rise to a cast of new stars and these stars possess significant market reach. Among them, fashion influencers—or widely followed digital content creators—stand out for their striking appearance and sartorial savvy. Acting as intermediaries and arbiters of taste, fashion influencers position products for social media users and help to shape their sale and promotion on behalf of brand partners online. Influencers’ ascent within the fashion industry has taken place alongside a broad scholarly debate on the democratic and inclusive potentials of social media applications, raising a number of questions related to contemporary changes in the organization and nature of labour online, and the range of ways in which platforms both challenge and reinforce existing inequalities and social privileges. How is labour managed and coordinated online, and against what constraints? In what ways is this labour rewarded? And, to whom do these rewards accrue? With these questions in mind, I turn to three empirical cases for study. Combining insights from in-depth interviews with influencers and the industry personnel who surround them with qualitative comments in reply to influencers’ content, I map the taken-for-granted avenues through which influencers’ labour and online presentation both reinforces and challenges existing inequalities. Though social media platforms can provide important opportunities for visibility to marginalized people, those who embody existing social privileges remain the most likely to receive lucrative brand work and product sponsorships. They are also more likely to garner symbolic wealth in the form of highly visible metrics including likes, comments, and follows. Influencers of colour and others who diverge from widely shared appearance norms, meanwhile, report significant obstacles to visibility online. For their part, talent agents and brand representatives de-centre matters related to inequality in their discussions on visibility and labour, appealing toward the importance of authenticity among influencers. These appeals toward authenticity, I argue, eclipse broader issues surrounding influencers’ labour and visibility, including the role that industry personnel play in moderating and constraining opportunities for diversity and inclusion online.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.036 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".