Balancing the Paradox of Inclusivity and Exclusivity: How Luxury Fashion Brands Use Communications to Address Diversity and Inclusion
Bibliographic record
Abstract
In the last decade, luxury fashion brands have been critiqued for design and marketing blunders involving racist imagery and cultural appropriation. These incidents were amplified during the Black Lives Matter protests in 2020, when luxury fashion brands that purportedly stood in solidarity with the movement were called out for past mistakes by a consumer base that is increasingly concerned with Corporate Social Responsibility and being “woke.” In moments of cultural and racial reckoning, luxury fashion brands are faced with a paradox: communicating values of inclusion while maintaining their exclusiveness. Using a case study approach focusing on Gucci, Prada and Burberry, this research analyzes the wording and structure behind communications of luxury fashion brands, particularly in regards to diversity and inclusion. Findings conclude that brands must follow a specific apology or acknowledgment structure when speaking about a crisis, but also reflect those communications internally to be deemed authentic by consumers.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".