“It’s All About the Look”: Making Sense of Appearance, Attractiveness, and Authenticity Online
Bibliographic record
Abstract
Existing research on Instagram suggests that the mobile application is dominated by a cast of hegemonically attractive influencers. But calls for greater diversity and inclusion are on the rise, raising important questions about how social media influencers and the industry personnel who support them understand diversity and negotiate appearance ideals. Drawing on 40 interviews with influencers and industry personnel as well as a year of online observation, I find that hegemonic ideals surrounding appearance and attractiveness continue to shape who is (and isn’t) perceived as worthy of visibility online. Industry personnel and influencers alike share that these ideals play an important role in cultivating a following on social media but remain convinced that change is underway. Emphasizing the importance of influencers’ relatability and perceived authenticity, influencers, agents, and brand representatives shift focus away from broader issues surrounding the platform including the role that industry personnel play in moderating and constraining opportunities for diversity 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".