Overcoming Exclusion and Discrimination in Fashion: An Exploratory Study on Diversity, Equity, and Inclusivity Initiatives
Bibliographic record
Abstract
<p>Diversity, Equity, and Inclusivity (DEI) initiatives are becoming an increasingly popular choice by fashion industry brands and companies in efforts to eliminate discrimination and unconscious racial bias in a professional capacity. This study explores the various motivations behind implementing these initiatives among a growing diverse workforce in a socially conscious era. Using a qualitative approach grounded in Critical Race Theory and Discursive Semiotics and interviewing four participants with experience working in the fashion industry, the research examines the efficacy of DEI initiatives. Including a comparative analysis on current DEI tactics employed by popular fashion brands Gucci and Ralph Lauren Corporation. The study reveals that popular tactics utilized while discrimination and unconscious bias are an industry concern are superficial in nature. For DEI initiatives to be successful in their goals, a long-term commitment must be made and implemented on all horizontal and vertical levels of a company to ensure its efficacy.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.018 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".