From Streetwear to High Fashion: How Nonelite Producers Use Hybridization to Enter an Elite Category
Bibliographic record
Abstract
Gaining access to elite categories is challenging for nonelite producers. This longitudinal analysis of the evolution of the fashion market explains how some nonelite streetwear producers gained access to the elite high fashion category by creating an interstitial hybrid category-luxury streetwear-that bridged the two. Nonelite producers create a hybrid category by establishing commensurability with the elite category and generating appeal through cultural consonance. The hybrid category challenges the elite boundary while potentializing value creation for elites. In response, elite producers subsume the hybrid category, which resolves the challenge posed by nonelite producers and allows elites to capture the value introduced by nonelites by absorbing and exerting control over them. By doing so, elite producers rejuvenate their category's cultural relevance and strengthen its elite status, while some nonelite producers gain access to the elite category. This research contributes to the literature by theorizing nonelites' status gains through categorical hybridization, an empirically prevalent but undertheorized phenomenon. These theoretical insights are leveraged to explain why subsumption solves cooptation issues, distinguish between collective and collaborative market driving, suggest that market actors can concomitantly participate in market maintenance and change, and offer managerial recommendations for nonelites to solidify status gains.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".