Navigating the Beauty Bind: Young People’s Intersectional Perspectives on Appearance, Privilege and Inequality
Bibliographic record
Abstract
The digital age exposes young people to a celebrity-saturated ‘beauty regime’ that reinforces ideals of physical perfection. Cultural sociologists and feminist scholars have highlighted the role of appearance as an important dimension of social stratification and demonstrated the prominence of celebrity images in popular culture and everyday imaginings. While beauty is increasingly recognized as an important element of culture and inequality, research is lacking on how young people understand the contemporary beauty regime and its intersectional complexities. This study explores how diverse Canadian youth navigate this complex landscape, focusing on their interpretations of beauty icon and billionaire Kylie Jenner. We draw from focus groups centred on the following question: How do young people understand beauty and its relationship to privilege and inequality? Our discussions highlight the intersectional nature of beauty and reveal three antinomies that young people navigate in the current beauty regime: (1) an aesthetic tension between fake and natural beauty; (2) a relational tension between elite beauty and democratic accessibility, and (3) a moral tension between looking good and being bad. The beauty bind describes the delicate balancing act young people face when navigating these tensions. Through an intersectional analysis, we aim to deepen scholarly understanding of beauty culture’s evolving dynamics, young people’s understandings of a celebritized beauty regime, and how beauty emerges as a powerful ideal that commands attention even as it often feels painfully out of reach.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.025 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".