Bibliographic record
Abstract
This chapter studies why luxury brands partner with artists through an historical overview of such alliances and a case study of Louis Vuitton, which has collaborated with an unusually varied range of artists. Fewer media have been offered a more effective fit with the world of luxury-brand marketing than synergistic partnerships with artists. The artists gain increased exposure and, should they wish, a market-blessed legitimacy; the brand benefits from a perception of épater la bourgeois legitimacy—and thus authenticity—through its alliances with street-wise avatars of youth, rebellion, and cutting-edge modernism. Additionally, the chapter discusses the appropriation of luxury-brand artisanship, styles, and logos by street fashion designers and artists; participating in partnerships with the brands themselves; and spurring the rise of diversity within the industry. Further, it explores the concept of artification and the rise of luxury-brand museums. In closing, the chapter addresses potential future directions for partnerships between luxury brands and artists, taking into account the evolving role of luxury fashion in today’s new brand of artist, the multihyphenated megacelebrity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".