Research on Chinese Audience's Perception of Online Fashion Week under the Influence of COVID-19
Bibliographic record
Abstract
Due to COVID-19, numerous offline events could not be hold as scheduled due to the restrictions of the quarantine of the pandemic, and this was also the case for the fashion industry. The 2022 Shanghai Fashion Week therefore opted for a completely online format, an unprecedented form innovation that is new to the industry. From augmented reality shows to meta-verse spaces, the fashion show uses digital technologies to express newest fashion to audiences. Although previous research has studied the audience reception of fashion weeks in China, few are tailored toward purely online fashion weeks. This research analyzes the attitudes of Chinese audiences towards online fashion weeks in the post-pandemic context. The research primarily uses surveys and interviews to obtain the necessary information, with secondary data from 2019 to 2022 collected over the internet. The study finds that on one hand, with its ease of access and with the influence of social media, online fashion week can have a larger exposure than offline. On the other hand, online shows are not a comprehensive presentation of clothes. Because viewers are not able to feel the clothes firsthand, the sales will be negatively affected. Therefore, the combination of "online + offline" fashion shows, having both the viral influence of online and the tangible feel of offline, may be the best of both worlds in the post-epidemic era.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".