The Influence of the COVID-19 Virus on the Luxury Retail Market in 2020
Bibliographic record
Abstract
On March 5, 2020 retailers' top priority has been the global spread of COVID-19 in recent weeks, which had a direct effect on the health and safety of their workers and customers. Those concerns early in the year steered many retailers, to close their stores within China and constrain employee travel. Several weeks later, the virus has reached the U.S and other countries outside China, with 93,000 cases tracked worldwide as of March 4, 2020 according to the World Organization. Many details about the disease, which has flu-like symptoms remain unknown, and health officials are still investigating how far and for how long it might spread. But as more cases of the disease have been reported globally, problems for the retail industry have materialized. For mass merchants like Walmart, the virus could have positive effects, since they are sources of groceries and disease-fighting essentials that consumers. However, the luxury retail market has suffered tremendously. One of the biggest signs of the industry's response has been the closure of fashion weeks. and many Luxury houses have closed their stores and canceled their shows. This Research paper will investigate the crisis of COVID -19 and its influence on the luxury retail market particularly.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".