Prioritizing Geo-References: A Content Analysis of the Websites of Leading Global Luxury Fashion Brands
Bibliographic record
Abstract
The current study investigates the extent to which various types of references to geographic entities are being used in communications with consumers on global websites of 15 leading luxury fashion brands. Using the Relevance-Distinctiveness-Believability framework, we posit that references to country of brand origin and to relatedness to developed Western countries are the most distinct and relevant and will be used more frequently than references to brand globalness and non-Western developed countries. We also suggest that Caucasian (i.e., “White”) models will be depicted more frequently than other ethnicities to further connect brands to Western countries. The analysis of 3,750 explicit-verbal, implicit-verbal, and non-verbal geo-references supports our predictions. These results suggest that, at least for the luxury fashion industry, the value of references to the country of brand origin and to the brand's relatedness to Western countries is substantially higher than the value of brand globalness. The article discusses the applicability of the RDB framework for prioritizing different types of geo-references and suggests venues for future research.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".